#load("vcomball20210902.Rda")
load(path(here::here("InitalDataCleaning/Data/vcomball20210902.Rda")))
d <- vcomball
# load("vsurvall20210902.Rda")
# d <- vsurvall
#load("vsiteid20210601.Rda")
new.d <- data.frame(matrix(ncol=0, nrow=nrow(d)))
new.d.1 <- data.frame(matrix(ncol=0, nrow=nrow(d)))
SITE ID
- Codes(based on Surveyid)
- 10 Greater CA
- 20 Georgia
- 25 North Carolina
- 30 Northern CA
- 40 Louisiana
- 50 New Jersey
- 60 Detroit
- 61 Michigan
- 70 Texas
- 80 Los Angeles County
- 81 USC-Other
- 82 USC-MEC
- 90 New York
- 94 Florida
- 95 WebRecruit-Limbo
- 99 WebRecruit
siteid <- as.factor(trimws(d[,"siteid"]))
#new.d.n <- data.frame(new.d.n, siteid) # keep NAACCR coding
levels(siteid)[levels(siteid)=="80"] <- "Los Angeles County.80"
levels(siteid)[levels(siteid)=="30"] <- "Northern CA.30"
levels(siteid)[levels(siteid)=="10"] <- "Greater CA.10"
levels(siteid)[levels(siteid)=="60"] <- "Detroit.60"
levels(siteid)[levels(siteid)=="40"] <- "Louisiana.40"
levels(siteid)[levels(siteid)=="20"] <- "Georgia.20"
levels(siteid)[levels(siteid)=="61"] <- "Michigan.61"
levels(siteid)[levels(siteid)=="50"] <- "New Jersey.50"
levels(siteid)[levels(siteid)=="70"] <- "Texas.70"
levels(siteid)[levels(siteid)=="99"] <- "WebRecruit.99"
levels(siteid)[levels(siteid)=="21"] <- "Georgia.21"
levels(siteid)[levels(siteid)=="81"] <- "USC Other.81"
levels(siteid)[levels(siteid)=="82"] <- "USC MEC.82"
siteid_new<- siteid
d<-data.frame(d, siteid_new)
new.d <- data.frame(new.d, siteid)
new.d <- apply_labels(new.d, siteid = "Site ID")
new.d.1 <- data.frame(new.d.1, siteid)
siteid_count<-count(new.d$siteid)
colnames(siteid_count)<- c("Registry", "Total")
kable(siteid_count, format = "simple", align = 'l', caption = "Overview of all Registries")
d<-d[which(d$siteid_new == params$site),]
new.d <- data.frame(matrix(ncol=0, nrow=nrow(d)))
#new.d<-new.d[which(new.d$siteid == params$site),]
SURVEY ID
- Scantron assigned SurveyID
surveyid <- as.factor(d[,"surveyid"])
isDup <- duplicated(surveyid)
numDups <- sum(isDup)
dups <- surveyid[isDup]
new.d <- data.frame(new.d, surveyid)
new.d <- apply_labels(new.d, surveyid = "Survey ID")
print(paste("Number of duplicates:", numDups))
## [1] "Number of duplicates: 0"
print("The following are duplicated IDs:")
## [1] "The following are duplicated IDs:"
print(dups)
## factor(0)
## Levels:
print("Number of NAs:")
## [1] "Number of NAs:"
print(sum(is.na(new.d$surveyid)))
## [1] 0
LOCATION NAME
- Name of Registry delivery location
locationname <- as.factor(d[,"locationname"])
new.d <- data.frame(new.d, locationname)
new.d <- apply_labels(new.d, locationname = "Recruitment Location")
temp.d <- data.frame (new.d, locationname)
result<-questionr::freq(temp.d$locationname, total = TRUE)
#Create a NICE table
kable(result, format = "simple", align = 'l', caption = "Overview of Registry delivery location")
Overview of Registry delivery location
| 0 |
0 |
0 |
NA |
RESPOND ID
- From Barcode label put on last page of survey by registries, identifies participant. ResponseID is assigned by the registries.
respondid <- as.factor(d[,"respondid"])
#remove NAs in respondid in order to avoid showing NAs in duplicated values
respondid_rm<-respondid[!is.na(respondid)]
isDup <- duplicated(respondid_rm)
numDups <- sum(isDup)
dups <- respondid_rm[isDup]
new.d <- data.frame(new.d, respondid)
new.d <- apply_labels(new.d, respondid = "RESPOND ID")
print(paste("Number of duplicates:", numDups))
## [1] "Number of duplicates: 0"
print("The following are duplicated IDs:")
## [1] "The following are duplicated IDs:"
print(dups)
## factor(0)
## Levels:
print("Number of NAs:")
## [1] "Number of NAs:"
print(sum(is.na(new.d$respondid)))
## [1] 0
METHODOLOGY
- How survey was completed
- P=Paper
- O=Online complete
st_css()
methodology <- as.factor(d[,"methodology"])
levels(methodology) <- list(Paper="P",
Online="O")
methodology <- ordered(methodology, c("Paper", "Online"))
new.d <- data.frame(new.d, methodology)
new.d <- apply_labels(new.d, methodology = "Methodology for Survey Completion")
temp.d <- data.frame (new.d, methodology)
result<-questionr::freq(temp.d$methodology, total = TRUE)
kable(result, format = "simple", align = 'l')
| Paper |
0 |
NaN |
NaN |
| Online |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
A1: Date of diagnosis
- A1. In what month and year were you first diagnosed with prostate cancer?
# a1month
a1month <- as.factor(d[,"a1month"])
new.d <- data.frame(new.d, a1month)
new.d <- apply_labels(new.d, a1month = "Month Diagnosed")
temp.d <- data.frame (new.d, a1month)
result<-questionr::freq(temp.d$a1month, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "A1:month diagnosed")
A1:month diagnosed
| 0 |
0 |
0 |
NA |
#count<-as.data.frame(table(new.d$a1month))
#colnames(count)<- c("a1month", "Total")
#freq1<-table(new.d$a1month)
#freq<-as.data.frame(round(prop.table(freq1),3))
#colnames(freq)<- c("a1month", "Freq")
#result<-merge(count, freq,by="a1month",sort=F)
#kable(result, format = "simple", align = 'l', caption = "A1:month diagnosed")
#a1year
tmp<-d[,"a1year"]
tmp[tmp=="15"]<-"2015"
a1year <- as.factor(tmp)
#levels(a1year)[levels(a1year)=="15"] <- "2015"
#a1year[a1year=="15"] <- "2015" # change "15" to "2015"
#a1year <- as.Date(a1year, format = "%Y")
#a1year <- relevel(a1year, ref="1914")
new.d <- data.frame(new.d, a1year)
new.d <- apply_labels(new.d, a1year = "Year Diagnosed")
temp.d <- data.frame (new.d, a1year)
result<-questionr::freq(temp.d$a1year, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "A1:year diagnosed")
A1:year diagnosed
| 0 |
0 |
0 |
NA |
#a1not
# 1=I have NEVER had prostate cancer
# 2=I HAVE or HAVE HAD prostate cancer
# (paper survey only had a bubble for “never had” so value set to 2 if bubble not marked)"
a1not <- as.factor(d[,"a1not"])
levels(a1not) <- list(NEVER_had_ProstateCancer="1",
HAVE_had_ProstateCancer="2")
new.d <- data.frame(new.d, a1not)
new.d <- apply_labels(new.d, a1not = "Not Diagnosed")
temp.d <- data.frame (new.d, a1not)
result<-questionr::freq(temp.d$a1not, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "A1:not diagnosed")
A1:not diagnosed
| NEVER_had_ProstateCancer |
0 |
NaN |
NaN |
| HAVE_had_ProstateCancer |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
A2: Identify as AA
- A2. Do you identify as Black or African American?
a2 <- as.factor(d[,"a2"])
# Make "*" to NA
a2[which(a2=="*")]<-"NA"
levels(a2) <- list(No="1",
Yes="2")
a2 <- ordered(a2, c("Yes","No"))
new.d <- data.frame(new.d, a2)
new.d <- apply_labels(new.d, a2 = "Month Diagnosed")
temp.d <- data.frame (new.d, a2)
result<-questionr::freq(temp.d$a2, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "A2")
A2
| Yes |
0 |
NaN |
NaN |
| No |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
A3: Black or African American group
- A3. If Yes: A2. Which Black or African American group(s) and other races/ethnicities do you identify with? Mark all that apply.
- A3_1: 1=Black/African American
- A3_2: 1=Nigerian
- A3_3: 1=Jamaican
- A3_4: 1=Ethiopian
- A3_5: 1=Haitian
- A3_6: 1=Somali
- a3_7: 1=Guyanese
- A3_8: 1=Creole
- A3_9: 1=West Indian
- A3_10: 1=Caribbean
- A3_11: 1=White
- A3_12: 1=Asian/Asian American
- A3_13: 1=Native American or American Indian or Alaskan Native
- A3_14: 1=Middle Eastern or North African
- A3_15: 1=Native Hawaiian or Pacific Islander
- A3_16: 1=Hispanic
- A3_17: 1=Latino
- A3_18: 1=Spanish
- A3_19: 1=Mexican/Mexican American
- A3_20: 1=Salvadoran
- A3_21: 1=Puerto Rican
- A3_22: 1=Dominican
- A3_23: 1=Columbian
- A3_24: 1=Other
a3_1 <- as.factor(d[,"a3_1"])
levels(a3_1) <- list(Black_African_American="1")
new.d <- data.frame(new.d, a3_1)
new.d <- apply_labels(new.d, a3_1 = "Black_African_American")
temp.d <- data.frame (new.d, a3_1)
result<-questionr::freq(temp.d$a3_1, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Black_African_American")
1. Black_African_American
| Black_African_American |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
a3_2 <- as.factor(d[,"a3_2"])
levels(a3_2) <- list(Nigerian="1")
new.d <- data.frame(new.d, a3_2)
new.d <- apply_labels(new.d, a3_2 = "Nigerian")
temp.d <- data.frame (new.d, a3_2)
result<-questionr::freq(temp.d$a3_2, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Nigerian")
2. Nigerian
| Nigerian |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
a3_3 <- as.factor(d[,"a3_3"])
levels(a3_3) <- list(Jamaican="1")
new.d <- data.frame(new.d, a3_3)
new.d <- apply_labels(new.d, a3_3 = "Jamaican")
temp.d <- data.frame (new.d, a3_3)
result<-questionr::freq(temp.d$a3_3, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Jamaican")
3. Jamaican
| Jamaican |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
a3_4 <- as.factor(d[,"a3_4"])
levels(a3_4) <- list(Ethiopian="1")
new.d <- data.frame(new.d, a3_4)
new.d <- apply_labels(new.d, a3_4 = "Ethiopian")
temp.d <- data.frame (new.d, a3_4)
result<-questionr::freq(temp.d$a3_4, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "4. Ethiopian")
4. Ethiopian
| Ethiopian |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
a3_5 <- as.factor(d[,"a3_5"])
levels(a3_5) <- list(Haitian="1")
new.d <- data.frame(new.d, a3_5)
new.d <- apply_labels(new.d, a3_5 = "Haitian")
temp.d <- data.frame (new.d, a3_5)
result<-questionr::freq(temp.d$a3_5, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "5. Haitian")
5. Haitian
| Haitian |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
a3_6 <- as.factor(d[,"a3_6"])
levels(a3_6) <- list(Somali="1")
new.d <- data.frame(new.d, a3_6)
new.d <- apply_labels(new.d, a3_6 = "Somali")
temp.d <- data.frame (new.d, a3_6)
result<-questionr::freq(temp.d$a3_6, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "6. Somali")
6. Somali
| Somali |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
a3_7 <- as.factor(d[,"a3_7"])
levels(a3_7) <- list(Guyanese="1")
new.d <- data.frame(new.d, a3_7)
new.d <- apply_labels(new.d, a3_7 = "Guyanese")
temp.d <- data.frame (new.d, a3_7)
result<-questionr::freq(temp.d$a3_7, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "7. Guyanese")
7. Guyanese
| Guyanese |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
a3_8 <- as.factor(d[,"a3_8"])
levels(a3_8) <- list(Creole="1")
new.d <- data.frame(new.d, a3_8)
new.d <- apply_labels(new.d, a3_8 = "Creole")
temp.d <- data.frame (new.d, a3_8)
result<-questionr::freq(temp.d$a3_8, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "8. Creole")
8. Creole
| Creole |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
a3_9 <- as.factor(d[,"a3_9"])
levels(a3_9) <- list(West_Indian="1")
new.d <- data.frame(new.d, a3_9)
new.d <- apply_labels(new.d, a3_9 = "West_Indian")
temp.d <- data.frame (new.d, a3_9)
result<-questionr::freq(temp.d$a3_9, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "9. West_Indian")
9. West_Indian
| West_Indian |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
a3_10 <- as.factor(d[,"a3_10"])
levels(a3_10) <- list(Caribbean="1")
new.d <- data.frame(new.d, a3_10)
new.d <- apply_labels(new.d, a3_10 = "Caribbean")
temp.d <- data.frame (new.d, a3_10)
result<-questionr::freq(temp.d$a3_10, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "10. Caribbean")
10. Caribbean
| Caribbean |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
a3_11 <- as.factor(d[,"a3_11"])
levels(a3_11) <- list(White="1")
new.d <- data.frame(new.d, a3_11)
new.d <- apply_labels(new.d, a3_11 = "White")
temp.d <- data.frame (new.d, a3_11)
result<-questionr::freq(temp.d$a3_11, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "11. White")
11. White
| White |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
a3_12 <- as.factor(d[,"a3_12"])
levels(a3_12) <- list(Asian="1")
new.d <- data.frame(new.d, a3_12)
new.d <- apply_labels(new.d, a3_12 = "Asian")
temp.d <- data.frame (new.d, a3_12)
result<-questionr::freq(temp.d$a3_12, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "12. Asian")
12. Asian
| Asian |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
a3_13 <- as.factor(d[,"a3_13"])
levels(a3_13) <- list(Native_Indian="1")
new.d <- data.frame(new.d, a3_13)
new.d <- apply_labels(new.d, a3_13 = "Native_Indian")
temp.d <- data.frame (new.d, a3_13)
result<-questionr::freq(temp.d$a3_13, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "13. Native_Indian")
13. Native_Indian
| Native_Indian |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
a3_14 <- as.factor(d[,"a3_14"])
levels(a3_14) <- list(Middle_Eastern_North_African="1")
new.d <- data.frame(new.d, a3_14)
new.d <- apply_labels(new.d, a3_14 = "Middle_Eastern_North_African")
temp.d <- data.frame (new.d, a3_14)
result<-questionr::freq(temp.d$a3_14, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "14. Middle_Eastern_North_African")
14. Middle_Eastern_North_African
| Middle_Eastern_North_African |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
a3_15 <- as.factor(d[,"a3_15"])
levels(a3_15) <- list(Native_Hawaiian_Pacific_Islander="1")
new.d <- data.frame(new.d, a3_15)
new.d <- apply_labels(new.d, a3_15 = "Native_Hawaiian_Pacific_Islander")
temp.d <- data.frame (new.d, a3_15)
result<-questionr::freq(temp.d$a3_15, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "15. Native_Hawaiian_Pacific_Islander")
15. Native_Hawaiian_Pacific_Islander
| Native_Hawaiian_Pacific_Islander |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
a3_16 <- as.factor(d[,"a3_16"])
levels(a3_16) <- list(Hispanic="1")
new.d <- data.frame(new.d, a3_16)
new.d <- apply_labels(new.d, a3_16 = "Hispanic")
temp.d <- data.frame (new.d, a3_16)
result<-questionr::freq(temp.d$a3_16, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "16. Hispanic")
16. Hispanic
| Hispanic |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
a3_17 <- as.factor(d[,"a3_17"])
levels(a3_17) <- list(Latino="1")
new.d <- data.frame(new.d, a3_17)
new.d <- apply_labels(new.d, a3_17 = "Latino")
temp.d <- data.frame (new.d, a3_17)
result<-questionr::freq(temp.d$a3_17, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "17. Latino")
17. Latino
| Latino |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
a3_18 <- as.factor(d[,"a3_18"])
levels(a3_18) <- list(Spanish="1")
new.d <- data.frame(new.d, a3_18)
new.d <- apply_labels(new.d, a3_18 = "Spanish")
temp.d <- data.frame (new.d, a3_18)
result<-questionr::freq(temp.d$a3_18, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "18. Spanish")
18. Spanish
| Spanish |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
a3_19 <- as.factor(d[,"a3_19"])
levels(a3_19) <- list(Mexican="1")
new.d <- data.frame(new.d, a3_19)
new.d <- apply_labels(new.d, a3_19 = "Mexican")
temp.d <- data.frame (new.d, a3_19)
result<-questionr::freq(temp.d$a3_19, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "19. Mexican")
19. Mexican
| Mexican |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
a3_20 <- as.factor(d[,"a3_20"])
levels(a3_20) <- list(Salvadoran="1")
new.d <- data.frame(new.d, a3_20)
new.d <- apply_labels(new.d, a3_20 = "Salvadoran")
temp.d <- data.frame (new.d, a3_20)
result<-questionr::freq(temp.d$a3_20, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "20. Salvadoran")
20. Salvadoran
| Salvadoran |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
a3_21 <- as.factor(d[,"a3_21"])
levels(a3_21) <- list(Puerto_Rican="1")
new.d <- data.frame(new.d, a3_21)
new.d <- apply_labels(new.d, a3_21 = "Puerto_Rican")
temp.d <- data.frame (new.d, a3_21)
result<-questionr::freq(temp.d$a3_21, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "21. Puerto_Rican")
21. Puerto_Rican
| Puerto_Rican |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
a3_22 <- as.factor(d[,"a3_22"])
levels(a3_22) <- list(Dominican="1")
new.d <- data.frame(new.d, a3_22)
new.d <- apply_labels(new.d, a3_22 = "Dominican")
temp.d <- data.frame (new.d, a3_22)
result<-questionr::freq(temp.d$a3_22, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "22. Dominican")
22. Dominican
| Dominican |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
a3_23 <- as.factor(d[,"a3_23"])
levels(a3_23) <- list(Columbian="1")
new.d <- data.frame(new.d, a3_23)
new.d <- apply_labels(new.d, a3_23 = "Columbian")
temp.d <- data.frame (new.d, a3_23)
result<-questionr::freq(temp.d$a3_23, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "23. Columbian")
23. Columbian
| Columbian |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
a3_24 <- as.factor(d[,"a3_24"])
levels(a3_23) <- list(Other="1")
new.d <- data.frame(new.d, a3_24)
new.d <- apply_labels(new.d, a3_24 = "Other")
temp.d <- data.frame (new.d, a3_24)
result<-questionr::freq(temp.d$a3_24, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "24. Other")
A3 Other: Black or African American group
a3other <- d[,"a3other"]
new.d <- data.frame(new.d, a3other)
new.d <- apply_labels(new.d, a3other = "A3Other")
temp.d <- data.frame (new.d, a3other)
result<-questionr::freq(temp.d$a3other, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "A3Other")
A4: Month and year of birth
A4. What is your month and year of birth?
# a4month
a4month <- as.factor(d[,"a4month"])
new.d <- data.frame(new.d, a4month)
new.d <- apply_labels(new.d, a4month = "Month of birth")
temp.d <- data.frame (new.d, a4month)
result<-questionr::freq(temp.d$a4month, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "A4: Month of birth")
A4: Month of birth
| 0 |
0 |
0 |
NA |
#a4year
a4year <- as.factor(d[,"a4year"])
new.d <- data.frame(new.d, a4year)
new.d <- apply_labels(new.d, a4year = "Year of birth")
temp.d <- data.frame (new.d, a4year)
result<-questionr::freq(temp.d$a4year, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "A4: Year of birth")
A4: Year of birth
| 0 |
0 |
0 |
NA |
A5: Where were you born
- A5. Where were you born?
- 1=United States (includes Hawaii and US territories)
- 2=Africa
- 3=Cuba or Caribbean Islands
- 4=Other
a5 <- as.factor(d[,"a5"])
# Make "*" to NA
a5[which(a5=="*")]<-"NA"
levels(a5) <- list(US="1",
Africa="2",
Cuba_Caribbean= "3",
Other="4")
a5 <- ordered(a5, c("US","Africa","Cuba_Caribbean","Other"))
new.d <- data.frame(new.d, a5)
new.d <- apply_labels(new.d, a5 = "Born place")
temp.d <- data.frame (new.d, a5)
result<-questionr::freq(temp.d$a5, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "A5: Where were you born?")
A5: Where were you born?
| US |
0 |
NaN |
NaN |
| Africa |
0 |
NaN |
NaN |
| Cuba_Caribbean |
0 |
NaN |
NaN |
| Other |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
A5 Other: Where were you born
a5other <- d[,"a5other"]
new.d <- data.frame(new.d, a5other)
new.d <- apply_labels(new.d, a5other = "a5other")
temp.d <- data.frame (new.d, a5other)
result<-questionr::freq(temp.d$a5other, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "A5Other")
A6: Biological father born
- A6. Where was your biological father born?
- 1=United States (includes Hawaii and US territories)
- 2=Africa
- 3=Cuba or Caribbean Islands
- 4=Other
a6 <- as.factor(d[,"a6"])
# Make "*" to NA
a6[which(a6=="*")]<-"NA"
levels(a6) <- list(US="1",
Africa="2",
Cuba_Caribbean= "3",
Other="4")
a6 <- ordered(a6, c("US","Africa","Cuba_Caribbean","Other"))
new.d <- data.frame(new.d, a6)
new.d <- apply_labels(new.d, a6 = "Born place")
temp.d <- data.frame (new.d, a6)
result<-questionr::freq(temp.d$a6, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "a6: Where were you born?")
a6: Where were you born?
| US |
0 |
NaN |
NaN |
| Africa |
0 |
NaN |
NaN |
| Cuba_Caribbean |
0 |
NaN |
NaN |
| Other |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
A6 Other: Biological father born
a6other <- d[,"a6other"]
new.d <- data.frame(new.d, a6other)
new.d <- apply_labels(new.d, a6other = "a6other")
temp.d <- data.frame (new.d, a6other)
result<-questionr::freq(temp.d$a6other, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "A6Other")
A7: Biological mother born
- A7. Where was your biological mother born?
- 1=United States (includes Hawaii and US territories)
- 2=Africa
- 3=Cuba or Caribbean Islands
- 4=Other
a7 <- as.factor(d[,"a7"])
# Make "*" to NA
a7[which(a7=="*")]<-"NA"
levels(a7) <- list(US="1",
Africa="2",
Cuba_Caribbean= "3",
Other="4")
a7 <- ordered(a7, c("US","Africa","Cuba_Caribbean","Other"))
new.d <- data.frame(new.d, a7)
new.d <- apply_labels(new.d, a7 = "Born place")
temp.d <- data.frame (new.d, a7)
result<-questionr::freq(temp.d$a7, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "a7: Where were you born?")
a7: Where were you born?
| US |
0 |
NaN |
NaN |
| Africa |
0 |
NaN |
NaN |
| Cuba_Caribbean |
0 |
NaN |
NaN |
| Other |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
A7 Other: Biological father born
a7other <- d[,"a7other"]
new.d <- data.frame(new.d, a7other)
new.d <- apply_labels(new.d, a7other = "a7other")
temp.d <- data.frame (new.d, a7other)
result<-questionr::freq(temp.d$a7other, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "A7Other")
A8: Years lived in the US
- A8. How many years have you lived in the United States?
- 1=15 years or less
- 2=16-25 years
- 3=My whole life or more than 25 years
a8 <- as.factor(d[,"a8"])
# Make "*" to NA
a8[which(a8=="*")]<-"NA"
levels(a8) <- list(less_or_15="1",
years_16_25="2",
more_than_25_or_whole_life= "3")
a8 <- ordered(a8, c("less_or_15","years_16_25","more_than_25_or_whole_life"))
new.d <- data.frame(new.d, a8)
new.d <- apply_labels(new.d, a8 = "Years lived in the US")
temp.d <- data.frame (new.d, a8)
result<-questionr::freq(temp.d$a8, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "A8")
A8
| less_or_15 |
0 |
NaN |
NaN |
| years_16_25 |
0 |
NaN |
NaN |
| more_than_25_or_whole_life |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
B1A: Father
- B1Aa: Father: Has this person had prostate cancer?
- B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
- B1Ac: Father: Did he (or any) die of prostate cancer?
# B1Aa: Father: Has this person had prostate cancer?
b1aa <- as.factor(d[,"b1aa"])
# Make "*" to NA
b1aa[which(b1aa=="*")]<-"NA"
levels(b1aa) <- list(No="1",
Yes="2",
Dont_know="88")
b1aa <- ordered(b1aa, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1aa)
new.d <- apply_labels(new.d, b1aa = "Father")
temp.d <- data.frame (new.d, b1aa)
result<-questionr::freq(temp.d$b1aa,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Aa: Father: Has this person had prostate cancer?")
B1Aa: Father: Has this person had prostate cancer?
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
b1ab <- as.factor(d[,"b1ab"])
# Make "*" to NA
b1ab[which(b1ab=="*")]<-"NA"
levels(b1ab) <- list(No="1",
Yes="2",
Dont_know="88")
b1ab <- ordered(b1ab, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1ab)
new.d <- apply_labels(new.d, b1ab = "Father")
temp.d <- data.frame (new.d, b1ab)
result<-questionr::freq(temp.d$b1ab,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?")
B1Ab: Father: Was he (or any) diagnosed BEFORE age 55?
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#B1Ac: Father: Did he (or any) die of prostate cancer?
b1ac <- as.factor(d[,"b1ac"])
# Make "*" to NA
b1ac[which(b1ac=="*")]<-"NA"
levels(b1ac) <- list(No="1",
Yes="2",
Dont_know="88")
b1ac <- ordered(b1ac, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1ac)
new.d <- apply_labels(new.d, b1ac = "Father")
temp.d <- data.frame (new.d, b1ac)
result<-questionr::freq(temp.d$b1ac,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Ac: Father: Did he (or any) die of prostate cancer?")
B1Ac: Father: Did he (or any) die of prostate cancer?
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
B1B: Any Brother
- B1BNo: Any Brother
- 1=I had no brothers
- if not marked
- B1Ba: Any Brother: Has this person had prostate cancer?
- B1Ba2: Any Brother: If Yes, number with prostate cancer
- B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
- B1Bc: Any Brother: Did he (or any) die of prostate cancer?
# B1BNo: Any Brother
b1bno <- as.factor(d[,"b1bno"])
levels(b1bno) <- list(No_brothers="1")
new.d <- data.frame(new.d, b1bno)
new.d <- apply_labels(new.d, b1bno = "Any Brother")
temp.d <- data.frame (new.d, b1bno)
result<-questionr::freq(temp.d$b1bno,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1BNo: Any Brother")
B1BNo: Any Brother
| No_brothers |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#B1Ba: Any Brother: Has this person had prostate cancer?
b1ba <- as.factor(d[,"b1ba"])
# Make "*" to NA
b1ba[which(b1ba=="*")]<-"NA"
levels(b1ba) <- list(No="1",
Yes="2",
Dont_know="88")
b1ba <- ordered(b1ba, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1ba)
new.d <- apply_labels(new.d, b1ba = "Any Brother: have p cancer")
temp.d <- data.frame (new.d, b1ba)
result<-questionr::freq(temp.d$b1ba,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Ba: Any Brother: Has this person had prostate cancer?")
B1Ba: Any Brother: Has this person had prostate cancer?
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#B1Ba2: Any Brother: If Yes, number with prostate cancer
b1ba2 <- as.factor(d[,"b1ba2"])
# Make "*" to NA
b1ba2[which(b1ba2=="*")]<-"NA"
levels(b1ba2) <- list(One="1",
Two_or_more="2")
b1ba2 <- ordered(b1ba2, c("One","Two_or_more"))
new.d <- data.frame(new.d, b1ba2)
new.d <- apply_labels(new.d, b1ba2 = "Number of brother")
temp.d <- data.frame (new.d, b1ba2)
result<-questionr::freq(temp.d$b1ba2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Ba2: Any Brother: If Yes, number with prostate cancer")
B1Ba2: Any Brother: If Yes, number with prostate cancer
| One |
0 |
NaN |
NaN |
| Two_or_more |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
b1bb <- as.factor(d[,"b1bb"])
# Make "*" to NA
b1bb[which(b1bb=="*")]<-"NA"
levels(b1bb) <- list(No="1",
Yes="2",
Dont_know="88")
b1bb <- ordered(b1bb, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1bb)
new.d <- apply_labels(new.d, b1bb = "Any Brother: before 55")
temp.d <- data.frame (new.d, b1bb)
result<-questionr::freq(temp.d$b1bb,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?")
B1Bb: Any Brother: Was he (or any) diagnosed BEFORE age 55?
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#B1Bc: Any Brother: Did he (or any) die of prostate cancer?
b1bc <- as.factor(d[,"b1bc"])
# Make "*" to NA
b1bc[which(b1bc=="*")]<-"NA"
levels(b1bc) <- list(No="1",
Yes="2",
Dont_know="88")
b1bc <- ordered(b1bc, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1bc)
new.d <- apply_labels(new.d, b1bc = "Any Brother: die")
temp.d <- data.frame (new.d, b1bc)
result<-questionr::freq(temp.d$b1bc,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Bc: Any Brother: Did he (or any) die of prostate cancer?")
B1Bc: Any Brother: Did he (or any) die of prostate cancer?
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
B1C: Any Son
- B1CNo: Any Son
- 1=I had no sons
- if not marked
- B1Ca: Any Son: Has this person had prostate cancer?
- B1Ca2: Any Son: If Yes, number with prostate cancer
- B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
- B1Cc: Any Son: Did he (or any) die of prostate cancer?
# B1BNo
b1cno <- as.factor(d[,"b1cno"])
levels(b1cno) <- list(No_brothers="1")
new.d <- data.frame(new.d, b1cno)
new.d <- apply_labels(new.d, b1cno = "Any Son")
temp.d <- data.frame (new.d, b1cno)
result<-questionr::freq(temp.d$b1cno,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1CNo: Any Son")
B1CNo: Any Son
| No_brothers |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#B1Ca
b1ca <- as.factor(d[,"b1ca"])
# Make "*" to NA
b1ca[which(b1ca=="*")]<-"NA"
levels(b1ca) <- list(No="1",
Yes="2",
Dont_know="88")
b1ca <- ordered(b1ca, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1ca)
new.d <- apply_labels(new.d, b1ca = "Any Son: have p cancer")
temp.d <- data.frame (new.d, b1ca)
result<-questionr::freq(temp.d$b1ca,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Ca: Any Son: Has this person had prostate cancer?")
B1Ca: Any Son: Has this person had prostate cancer?
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#B1Ca2
b1ca2 <- as.factor(d[,"b1ca2"])
# Make "*" to NA
b1ca2[which(b1ca2=="*")]<-"NA"
levels(b1ca2) <- list(One="1",
Two_or_more="2")
b1ca2 <- ordered(b1ca2, c("One","Two_or_more"))
new.d <- data.frame(new.d, b1ca2)
new.d <- apply_labels(new.d, b1ca2 = "Number of sons")
temp.d <- data.frame (new.d, b1ca2)
result<-questionr::freq(temp.d$b1ca2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Ca2: Any Son: If Yes, number with prostate cancer")
B1Ca2: Any Son: If Yes, number with prostate cancer
| One |
0 |
NaN |
NaN |
| Two_or_more |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#B1Cb
b1cb <- as.factor(d[,"b1cb"])
# Make "*" to NA
b1cb[which(b1cb=="*")]<-"NA"
levels(b1cb) <- list(No="1",
Yes="2",
Dont_know="88")
b1cb <- ordered(b1cb, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1cb)
new.d <- apply_labels(new.d, b1cb = "Any Son: before 55")
temp.d <- data.frame (new.d, b1cb)
result<-questionr::freq(temp.d$b1cb,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?")
B1Cb: Any Son: Was he (or any) diagnosed BEFORE age 55?
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#B1Cc
b1cc <- as.factor(d[,"b1cc"])
# Make "*" to NA
b1cc[which(b1cc=="*")]<-"NA"
levels(b1cc) <- list(No="1",
Yes="2",
Dont_know="88")
b1cc <- ordered(b1cc, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1cc)
new.d <- apply_labels(new.d, b1cc = "Any Son: die")
temp.d <- data.frame (new.d, b1cc)
result<-questionr::freq(temp.d$b1cc,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Cc: Any Son: Did he (or any) die of prostate cancer?")
B1Cc: Any Son: Did he (or any) die of prostate cancer?
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
B1D: Maternal Grandfather
- B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
- B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
- b1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
# B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
b1da <- as.factor(d[,"b1da"])
# Make "*" to NA
b1da[which(b1da=="*")]<-"NA"
levels(b1da) <- list(No="1",
Yes="2",
Dont_know="88")
b1da <- ordered(b1da, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1da)
new.d <- apply_labels(new.d, b1da = "Father")
temp.d <- data.frame (new.d, b1da)
result<-questionr::freq(temp.d$b1da,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?")
B1Da: Maternal Grandfather (Mom’s side): Has this person had prostate cancer?
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
# B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
b1db <- as.factor(d[,"b1db"])
# Make "*" to NA
b1db[which(b1db=="*")]<-"NA"
levels(b1db) <- list(No="1",
Yes="2",
Dont_know="88")
b1db <- ordered(b1db, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1db)
new.d <- apply_labels(new.d, b1db = "Father")
temp.d <- data.frame (new.d, b1db)
result<-questionr::freq(temp.d$b1db,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?")
B1Db: Maternal Grandfather (Mom’s side): Was he (or any) diagnosed BEFORE age 55?
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
# B1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
b1dc <- as.factor(d[,"b1dc"])
# Make "*" to NA
b1dc[which(b1dc=="*")]<-"NA"
levels(b1dc) <- list(No="1",
Yes="2",
Dont_know="88")
b1dc <- ordered(b1dc, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1dc)
new.d <- apply_labels(new.d, b1dc = "Father")
temp.d <- data.frame (new.d, b1dc)
result<-questionr::freq(temp.d$b1dc,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?")
B1Dc: Maternal Grandfather (Mom’s side): Did he (or any) die of prostate cancer?
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
B1E: Paternal Grandfather
- B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
- B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
- B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
# B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
b1ea <- as.factor(d[,"b1ea"])
# Make "*" to NA
b1ea[which(b1ea=="*")]<-"NA"
levels(b1ea) <- list(No="1",
Yes="2",
Dont_know="88")
b1ea <- ordered(b1ea, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1ea)
new.d <- apply_labels(new.d, b1ea = "Father")
temp.d <- data.frame (new.d, b1ea)
result<-questionr::freq(temp.d$b1ea,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?")
B1Ea: Paternal Grandfather (Dad’s side): Has this person had prostate cancer?
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
# B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
b1eb <- as.factor(d[,"b1eb"])
# Make "*" to NA
b1eb[which(b1eb=="*")]<-"NA"
levels(b1eb) <- list(No="1",
Yes="2",
Dont_know="88")
b1eb <- ordered(b1eb, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1eb)
new.d <- apply_labels(new.d, b1eb = "Father")
temp.d <- data.frame (new.d, b1eb)
result<-questionr::freq(temp.d$b1eb,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?")
B1Eb: Paternal Grandfather (Dad’s side): Was he (or any) diagnosed BEFORE age 55?
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
# B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
b1ec <- as.factor(d[,"b1ec"])
# Make "*" to NA
b1ec[which(b1ec=="*")]<-"NA"
levels(b1ec) <- list(No="1",
Yes="2",
Dont_know="88")
b1ec <- ordered(b1ec, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, b1ec)
new.d <- apply_labels(new.d, b1ec = "Father")
temp.d <- data.frame (new.d, b1ec)
result<-questionr::freq(temp.d$b1ec,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?")
B1Ec: Paternal Grandfather (Dad’s side): Did he (or any) die of prostate cancer?
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
B2: Family History (Other cancers)
- B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)?
b2 <- as.factor(d[,"b2"])
# Make "*" to NA
b2[which(b2=="*")]<-"NA"
levels(b2) <- list(No="1",
Yes="2")
b2 <- ordered(b2, c("Yes","No"))
new.d <- data.frame(new.d, b2)
new.d <- apply_labels(new.d, b2 = "Month Diagnosed")
temp.d <- data.frame (new.d, b2)
result<-questionr::freq(temp.d$b2, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B2")
B2
| Yes |
0 |
NaN |
NaN |
| No |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
B2A: Mother
- B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
- B2A_1: 1=Breast
- B2A_2: 1=Ovarian
- B2A_3: 1=Colorectal
- B2A_4: 1=Lung
- B2A_5: 1=Other Cancer
b2a_1 <- as.factor(d[,"b2a_1"])
levels(b2a_1) <- list(Breast="1")
new.d <- data.frame(new.d, b2a_1)
new.d <- apply_labels(new.d, b2a_1 = "Breast")
temp.d <- data.frame (new.d, b2a_1)
result<-questionr::freq(temp.d$b2a_1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
| Breast |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b2a_2 <- as.factor(d[,"b2a_2"])
levels(b2a_2) <- list(Ovarian="1")
new.d <- data.frame(new.d, b2a_2)
new.d <- apply_labels(new.d, b2a_2 = "Ovarian")
temp.d <- data.frame (new.d, b2a_2)
result<-questionr::freq(temp.d$b2a_2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Ovarian")
2. Ovarian
| Ovarian |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b2a_3 <- as.factor(d[,"b2a_3"])
levels(b2a_3) <- list(Colorectal="1")
new.d <- data.frame(new.d, b2a_3)
new.d <- apply_labels(new.d, b2a_3 = "Colorectal")
temp.d <- data.frame (new.d, b2a_3)
result<-questionr::freq(temp.d$b2a_3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
| Colorectal |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b2a_4 <- as.factor(d[,"b2a_4"])
levels(b2a_4) <- list(Lung="1")
new.d <- data.frame(new.d, b2a_4)
new.d <- apply_labels(new.d, b2a_4 = "Lung")
temp.d <- data.frame (new.d, b2a_4)
result<-questionr::freq(temp.d$b2a_4,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
| Lung |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b2a_5 <- as.factor(d[,"b2a_5"])
levels(b2a_5) <- list(Other_Cancer="1")
new.d <- data.frame(new.d, b2a_5)
new.d <- apply_labels(new.d, b2a_5 = "Lung")
temp.d <- data.frame (new.d, b2a_5)
result<-questionr::freq(temp.d$b2a_5,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
| Other_Cancer |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
B2B: Father
- B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
- B2B_1: 1=Breast
- B2B_3: 1=Colorectal
- B2B_4: 1=Lung
- B2B_5: 1=Other Cancer
b2b_1 <- as.factor(d[,"b2b_1"])
levels(b2b_1) <- list(Breast="1")
new.d <- data.frame(new.d, b2b_1)
new.d <- apply_labels(new.d, b2b_1 = "Breast")
temp.d <- data.frame (new.d, b2b_1)
result<-questionr::freq(temp.d$b2b_1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
| Breast |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b2b_3 <- as.factor(d[,"b2b_3"])
levels(b2b_3) <- list(Colorectal="1")
new.d <- data.frame(new.d, b2b_3)
new.d <- apply_labels(new.d, b2b_3 = "Colorectal")
temp.d <- data.frame (new.d, b2b_3)
result<-questionr::freq(temp.d$b2b_3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
| Colorectal |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b2b_4 <- as.factor(d[,"b2b_4"])
levels(b2b_4) <- list(Lung="1")
new.d <- data.frame(new.d, b2b_4)
new.d <- apply_labels(new.d, b2b_4 = "Lung")
temp.d <- data.frame (new.d, b2b_4)
result<-questionr::freq(temp.d$b2b_4,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
| Lung |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b2b_5 <- as.factor(d[,"b2b_5"])
levels(b2b_5) <- list(Other_Cancer="1")
new.d <- data.frame(new.d, b2b_5)
new.d <- apply_labels(new.d, b2b_5 = "Lung")
temp.d <- data.frame (new.d, b2b_5)
result<-questionr::freq(temp.d$b2b_5,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
| Other_Cancer |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
B2C: Any sister
- B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
- B2C_1: 1=Breast
- B2C_2: 1=Ovarian
- B2C_3: 1=Colorectal
- B2C_4: 1=Lung
- B2C_5: 1=Other Cancer
b2c_1 <- as.factor(d[,"b2c_1"])
levels(b2c_1) <- list(Breast="1")
new.d <- data.frame(new.d, b2c_1)
new.d <- apply_labels(new.d, b2c_1 = "Breast")
temp.d <- data.frame (new.d, b2c_1)
result<-questionr::freq(temp.d$b2c_1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
| Breast |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b2c_2 <- as.factor(d[,"b2c_2"])
levels(b2c_2) <- list(Ovarian="1")
new.d <- data.frame(new.d, b2c_2)
new.d <- apply_labels(new.d, b2c_2 = "Ovarian")
temp.d <- data.frame (new.d, b2c_2)
result<-questionr::freq(temp.d$b2c_2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Ovarian")
2. Ovarian
| Ovarian |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b2c_3 <- as.factor(d[,"b2c_3"])
levels(b2c_3) <- list(Colorectal="1")
new.d <- data.frame(new.d, b2c_3)
new.d <- apply_labels(new.d, b2c_3 = "Colorectal")
temp.d <- data.frame (new.d, b2c_3)
result<-questionr::freq(temp.d$b2c_3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
| Colorectal |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b2c_4 <- as.factor(d[,"b2c_4"])
levels(b2c_4) <- list(Lung="1")
new.d <- data.frame(new.d, b2c_4)
new.d <- apply_labels(new.d, b2c_4 = "Lung")
temp.d <- data.frame (new.d, b2c_4)
result<-questionr::freq(temp.d$b2c_4,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
| Lung |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b2c_5 <- as.factor(d[,"b2c_5"])
levels(b2c_5) <- list(Other_Cancer="1")
new.d <- data.frame(new.d, b2c_5)
new.d <- apply_labels(new.d, b2c_5 = "Lung")
temp.d <- data.frame (new.d, b2c_5)
result<-questionr::freq(temp.d$b2c_5,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
| Other_Cancer |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
B2D: Any brother
- B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
- B2D_1: 1=Breast
- B2D_3: 1=Colorectal
- B2D_4: 1=Lung
- B2D_5: 1=Other Cancer
b2d_1 <- as.factor(d[,"b2d_1"])
levels(b2d_1) <- list(Breast="1")
new.d <- data.frame(new.d, b2d_1)
new.d <- apply_labels(new.d, b2d_1 = "Breast")
temp.d <- data.frame (new.d, b2d_1)
result<-questionr::freq(temp.d$b2d_1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
| Breast |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b2d_3 <- as.factor(d[,"b2d_3"])
levels(b2d_3) <- list(Colorectal="1")
new.d <- data.frame(new.d, b2d_3)
new.d <- apply_labels(new.d, b2d_3 = "Colorectal")
temp.d <- data.frame (new.d, b2d_3)
result<-questionr::freq(temp.d$b2d_3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
| Colorectal |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b2d_4 <- as.factor(d[,"b2d_4"])
levels(b2d_4) <- list(Lung="1")
new.d <- data.frame(new.d, b2d_4)
new.d <- apply_labels(new.d, b2d_4 = "Lung")
temp.d <- data.frame (new.d, b2d_4)
result<-questionr::freq(temp.d$b2d_4,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
| Lung |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b2d_5 <- as.factor(d[,"b2d_5"])
levels(b2d_5) <- list(Other_Cancer="1")
new.d <- data.frame(new.d, b2d_5)
new.d <- apply_labels(new.d, b2d_5 = "Lung")
temp.d <- data.frame (new.d, b2d_5)
result<-questionr::freq(temp.d$b2d_5,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
| Other_Cancer |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
B2E: Any daughter
- B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
- B2E_1: 1=Breast
- B2E_2: 1=Ovarian
- B2E_3: 1=Colorectal
- B2E_4: 1=Lung
- B2E_5: 1=Other Cancer
b2e_1 <- as.factor(d[,"b2e_1"])
levels(b2e_1) <- list(Breast="1")
new.d <- data.frame(new.d, b2e_1)
new.d <- apply_labels(new.d, b2e_1 = "Breast")
temp.d <- data.frame (new.d, b2e_1)
result<-questionr::freq(temp.d$b2e_1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
| Breast |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b2e_2 <- as.factor(d[,"b2e_2"])
levels(b2e_2) <- list(Ovarian="1")
new.d <- data.frame(new.d, b2e_2)
new.d <- apply_labels(new.d, b2e_2 = "Ovarian")
temp.d <- data.frame (new.d, b2e_2)
result<-questionr::freq(temp.d$b2e_2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Ovarian")
2. Ovarian
| Ovarian |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b2e_3 <- as.factor(d[,"b2e_3"])
levels(b2e_3) <- list(Colorectal="1")
new.d <- data.frame(new.d, b2e_3)
new.d <- apply_labels(new.d, b2e_3 = "Colorectal")
temp.d <- data.frame (new.d, b2e_3)
result<-questionr::freq(temp.d$b2e_3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
| Colorectal |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b2e_4 <- as.factor(d[,"b2e_4"])
levels(b2e_4) <- list(Lung="1")
new.d <- data.frame(new.d, b2e_4)
new.d <- apply_labels(new.d, b2e_4 = "Lung")
temp.d <- data.frame (new.d, b2e_4)
result<-questionr::freq(temp.d$b2e_4,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
| Lung |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b2e_5 <- as.factor(d[,"b2e_5"])
levels(b2e_5) <- list(Other_Cancer="1")
new.d <- data.frame(new.d, b2e_5)
new.d <- apply_labels(new.d, b2e_5 = "Lung")
temp.d <- data.frame (new.d, b2e_5)
result<-questionr::freq(temp.d$b2e_5,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
| Other_Cancer |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
B2F: Any son
- B2. Other than prostate cancer, has any family member been diagnosed with one or more of these other cancers (only include biological or blood relatives)? If Yes, please indicate which family members had a cancer in the table below. Mark all that apply.
- B2F_1: 1=Breast
- B2F_3: 1=Colorectal
- B2F_4: 1=Lung
- B2F_5: 1=Other Cancer
b2f_1 <- as.factor(d[,"b2f_1"])
levels(b2f_1) <- list(Breast="1")
new.d <- data.frame(new.d, b2f_1)
new.d <- apply_labels(new.d, b2f_1 = "Breast")
temp.d <- data.frame (new.d, b2f_1)
result<-questionr::freq(temp.d$b2f_1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Breast")
1. Breast
| Breast |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b2f_3 <- as.factor(d[,"b2f_3"])
levels(b2f_3) <- list(Colorectal="1")
new.d <- data.frame(new.d, b2f_3)
new.d <- apply_labels(new.d, b2f_3 = "Colorectal")
temp.d <- data.frame (new.d, b2f_3)
result<-questionr::freq(temp.d$b2f_3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Colorectal")
3. Colorectal
| Colorectal |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b2f_4 <- as.factor(d[,"b2f_4"])
levels(b2f_4) <- list(Lung="1")
new.d <- data.frame(new.d, b2f_4)
new.d <- apply_labels(new.d, b2f_4 = "Lung")
temp.d <- data.frame (new.d, b2f_4)
result<-questionr::freq(temp.d$b2f_4,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "4. Lung")
4. Lung
| Lung |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b2f_5 <- as.factor(d[,"b2f_5"])
levels(b2f_5) <- list(Other_Cancer="1")
new.d <- data.frame(new.d, b2f_5)
new.d <- apply_labels(new.d, b2f_5 = "Lung")
temp.d <- data.frame (new.d, b2f_5)
result<-questionr::freq(temp.d$b2f_5,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "5. Other Cancer")
5. Other Cancer
| Other_Cancer |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
B3: Current health
- B3. In general, how would you rate your current health?
- 1=Excellent
- 2=Very Good
- 3=Good
- 4=Fair
- 5=Poor
b3 <- as.factor(d[,"b3"])
# Make "*" to NA
b3[which(b3=="*")]<-"NA"
levels(b3) <- list(Excellent="1",
Very_Good="2",
Good="3",
Fair="4",
Poor="5")
b3 <- ordered(b3, c("Excellent","Very_Good","Good","Fair","Poor"))
new.d <- data.frame(new.d, b3)
new.d <- apply_labels(new.d, b3 = "Current Health")
temp.d <- data.frame (new.d, b3)
result<-questionr::freq(temp.d$b3, cum = TRUE, total = TRUE)
kable(result, format = "simple", align = 'l')
| Excellent |
0 |
NaN |
NaN |
NaN |
NaN |
| Very_Good |
0 |
NaN |
NaN |
NaN |
NaN |
| Good |
0 |
NaN |
NaN |
NaN |
NaN |
| Fair |
0 |
NaN |
NaN |
NaN |
NaN |
| Poor |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
B4: Comorbidities
- B4. Has the doctor ever told you that you have/had…
- Heart Attack
- Heart Failure or CHF
- Stroke
- Hypertension
- Peripheral arterial disease
- High Cholesterol
- Asthma, COPD
- Stomach ulcers
- Crohn’s Disease
- Diabetes
- Kidney Problems
- Cirrhosis, liver damage
- Arthritis
- Dementia
- Depression
- AIDS
- Other Cancer
# Heart Attack
b4aa <- as.factor(d[,"b4aa"])
# Make "*" to NA
b4aa[which(b4aa=="*")]<-"NA"
levels(b4aa) <- list(No="1",
Yes="2")
b4aa <- ordered(b4aa, c("No", "Yes"))
new.d <- data.frame(new.d, b4aa)
new.d <- apply_labels(new.d, b4aa = "Heart Attack")
temp.d <- data.frame (new.d, b4aa)
result<-questionr::freq(temp.d$b4aa, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Heart Attack")
Heart Attack
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b4ab <- as.factor(d[,"b4ab"])
new.d <- data.frame(new.d, b4ab)
new.d <- apply_labels(new.d, b4ab = "Heart Attack age")
temp.d <- data.frame (new.d, b4ab)
result<-questionr::freq(temp.d$b4ab, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Heart Attack Age")
Heart Attack Age
| 0 |
0 |
0 |
NA |
# Heart Failure or CHF
b4ba <- as.factor(d[,"b4ba"])
# Make "*" to NA
b4ba[which(b4ba=="*")]<-"NA"
levels(b4ba) <- list(No="1",
Yes="2")
b4ba <- ordered(b4ba, c("No", "Yes"))
new.d <- data.frame(new.d, b4ba)
new.d <- apply_labels(new.d, b4ba = "Heart Failure or CHF")
temp.d <- data.frame (new.d, b4ba)
result<-questionr::freq(temp.d$b4ba, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Heart Failure or CHF")
Heart Failure or CHF
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b4bb <- as.factor(d[,"b4bb"])
new.d <- data.frame(new.d, b4bb)
new.d <- apply_labels(new.d, b4bb = "Heart Failure or CHF age")
temp.d <- data.frame (new.d, b4bb)
result<-questionr::freq(temp.d$b4bb, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Heart Failure or CHF Age")
Heart Failure or CHF Age
| 0 |
0 |
0 |
NA |
# Stroke
b4ca <- as.factor(d[,"b4ca"])
# Make "*" to NA
b4ca[which(b4ca=="*")]<-"NA"
levels(b4ca) <- list(No="1",
Yes="2")
b4ca <- ordered(b4ca, c("No", "Yes"))
new.d <- data.frame(new.d, b4ca)
new.d <- apply_labels(new.d, b4ca = "Stroke")
temp.d <- data.frame (new.d, b4ca)
result<-questionr::freq(temp.d$b4ca,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Stroke")
Stroke
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b4cb <- as.factor(d[,"b4cb"])
new.d <- data.frame(new.d, b4cb)
new.d <- apply_labels(new.d, b4cb = "Stroke age")
temp.d <- data.frame (new.d, b4cb)
result<-questionr::freq(temp.d$b4cb, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Stroke Age")
# Hypertension
b4da <- as.factor(d[,"b4da"])
# Make "*" to NA
b4da[which(b4da=="*")]<-"NA"
levels(b4da) <- list(No="1",
Yes="2")
b4da <- ordered(b4da, c("No", "Yes"))
new.d <- data.frame(new.d, b4da)
new.d <- apply_labels(new.d, b4da = "Hypertension")
temp.d <- data.frame (new.d, b4da)
result<-questionr::freq(temp.d$b4da, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Hypertension")
Hypertension
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b4db <- as.factor(d[,"b4db"])
new.d <- data.frame(new.d, b4db)
new.d <- apply_labels(new.d, b4db = "Hypertension age")
temp.d <- data.frame (new.d, b4db)
result<-questionr::freq(temp.d$b4db, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Hypertension Age")
Hypertension Age
| 0 |
0 |
0 |
NA |
# Peripheral arterial disease
b4ea <- as.factor(d[,"b4ea"])
# Make "*" to NA
b4ea[which(b4ea=="*")]<-"NA"
levels(b4ea) <- list(No="1",
Yes="2")
b4ea <- ordered(b4ea, c("No", "Yes"))
new.d <- data.frame(new.d, b4ea)
new.d <- apply_labels(new.d, b4ea = "Peripheral arterial disease")
temp.d <- data.frame (new.d, b4ea)
result<-questionr::freq(temp.d$b4ea,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Peripheral arterial disease")
Peripheral arterial disease
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b4eb <- as.factor(d[,"b4eb"])
new.d <- data.frame(new.d, b4eb)
new.d <- apply_labels(new.d, b4eb = "Peripheral arterial disease age")
temp.d <- data.frame (new.d, b4eb)
result<-questionr::freq(temp.d$b4eb, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Peripheral arterial disease Age")
Peripheral arterial disease Age
| 0 |
0 |
0 |
NA |
# High Cholesterol
b4fa <- as.factor(d[,"b4fa"])
# Make "*" to NA
b4fa[which(b4fa=="*")]<-"NA"
levels(b4fa) <- list(No="1",
Yes="2")
b4fa <- ordered(b4fa, c("No", "Yes"))
new.d <- data.frame(new.d, b4fa)
new.d <- apply_labels(new.d, b4fa = "High Cholesterol")
temp.d <- data.frame (new.d, b4fa)
result<-questionr::freq(temp.d$b4fa, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "High Cholesterol")
High Cholesterol
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b4fb <- as.factor(d[,"b4fb"])
new.d <- data.frame(new.d, b4fb)
new.d <- apply_labels(new.d, b4fb = "High Cholesterol age")
temp.d <- data.frame (new.d, b4fb)
result<-questionr::freq(temp.d$b4fb, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "High Cholesterol Age")
High Cholesterol Age
| 0 |
0 |
0 |
NA |
# Asthma, COPD
b4ga <- as.factor(d[,"b4ga"])
# Make "*" to NA
b4ga[which(b4ga=="*")]<-"NA"
levels(b4ga) <- list(No="1",
Yes="2")
b4ga <- ordered(b4ga, c("No", "Yes"))
new.d <- data.frame(new.d, b4ga)
new.d <- apply_labels(new.d, b4ga = "Asthma, COPD")
temp.d <- data.frame (new.d, b4ga)
result<-questionr::freq(temp.d$b4ga, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Asthma, COPD")
Asthma, COPD
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b4gb <- as.factor(d[,"b4gb"])
new.d <- data.frame(new.d, b4gb)
new.d <- apply_labels(new.d, b4gb = "Asthma, COPD age")
temp.d <- data.frame (new.d, b4gb)
result<-questionr::freq(temp.d$b4gb, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Asthma, COPD Age")
Asthma, COPD Age
| 0 |
0 |
0 |
NA |
# Stomach ulcers
b4ha <- as.factor(d[,"b4ha"])
# Make "*" to NA
b4ha[which(b4ha=="*")]<-"NA"
levels(b4ha) <- list(No="1",
Yes="2")
b4ha <- ordered(b4ha, c("No", "Yes"))
new.d <- data.frame(new.d, b4ha)
new.d <- apply_labels(new.d, b4ha = "Stomach ulcers")
temp.d <- data.frame (new.d, b4ha)
result<-questionr::freq(temp.d$b4ha, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Stomach ulcers")
Stomach ulcers
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b4hb <- as.factor(d[,"b4hb"])
new.d <- data.frame(new.d, b4hb)
new.d <- apply_labels(new.d, b4hb = "Stomach ulcers age")
temp.d <- data.frame (new.d, b4hb)
result<-questionr::freq(temp.d$b4hb, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Stomach ulcers Age")
Stomach ulcers Age
| 0 |
0 |
0 |
NA |
# Crohn's Disease
b4ia <- as.factor(d[,"b4ia"])
# Make "*" to NA
b4ia[which(b4ia=="*")]<-"NA"
levels(b4ia) <- list(No="1",
Yes="2")
b4ia <- ordered(b4ia, c("No", "Yes"))
new.d <- data.frame(new.d, b4ia)
new.d <- apply_labels(new.d, b4ia = "Crohn's Disease")
temp.d <- data.frame (new.d, b4ia)
result<-questionr::freq(temp.d$b4ia, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Crohn's Disease")
Crohn’s Disease
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b4ib <- as.factor(d[,"b4ib"])
new.d <- data.frame(new.d, b4ib)
new.d <- apply_labels(new.d, b4ib = "Crohn's Disease age")
temp.d <- data.frame (new.d, b4ib)
result<-questionr::freq(temp.d$b4ib, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Crohn's Disease Age")
Crohn’s Disease Age
| 0 |
0 |
0 |
NA |
# Diabetes
b4ja <- as.factor(d[,"b4ja"])
# Make "*" to NA
b4ja[which(b4ja=="*")]<-"NA"
levels(b4ja) <- list(No="1",
Yes="2")
b4ja <- ordered(b4ja, c("No", "Yes"))
new.d <- data.frame(new.d, b4ja)
new.d <- apply_labels(new.d, b4ja = "Diabetes")
temp.d <- data.frame (new.d, b4ja)
result<-questionr::freq(temp.d$b4ja, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Diabetes")
Diabetes
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b4jb <- as.factor(d[,"b4jb"])
new.d <- data.frame(new.d, b4jb)
new.d <- apply_labels(new.d, b4jb = "Diabetes age")
temp.d <- data.frame (new.d, b4jb)
result<-questionr::freq(temp.d$b4jb, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Diabetes Age")
# Kidney Problems
b4ka <- as.factor(d[,"b4ka"])
# Make "*" to NA
b4ka[which(b4ka=="*")]<-"NA"
levels(b4ka) <- list(No="1",
Yes="2")
b4ka <- ordered(b4ka, c("No", "Yes"))
new.d <- data.frame(new.d, b4ka)
new.d <- apply_labels(new.d, b4ka = "Kidney Problems")
temp.d <- data.frame (new.d, b4ka)
result<-questionr::freq(temp.d$b4ka, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Kidney Problems")
Kidney Problems
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b4kb <- as.factor(d[,"b4kb"])
new.d <- data.frame(new.d, b4kb)
new.d <- apply_labels(new.d, b4kb = "Kidney Problems age")
temp.d <- data.frame (new.d, b4kb)
result<-questionr::freq(temp.d$b4kb, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Kidney Problems Age")
Kidney Problems Age
| 0 |
0 |
0 |
NA |
# Cirrhosis, liver damage
b4la <- as.factor(d[,"b4la"])
# Make "*" to NA
b4la[which(b4la=="*")]<-"NA"
levels(b4la) <- list(No="1",
Yes="2")
b4la <- ordered(b4la, c("No", "Yes"))
new.d <- data.frame(new.d, b4la)
new.d <- apply_labels(new.d, b4la = "Cirrhosis, liver damage")
temp.d <- data.frame (new.d, b4la)
result<-questionr::freq(temp.d$b4la, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Cirrhosis, liver damage")
Cirrhosis, liver damage
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b4lb <- as.factor(d[,"b4lb"])
new.d <- data.frame(new.d, b4lb)
new.d <- apply_labels(new.d, b4lb = "Cirrhosis, liver damage age")
temp.d <- data.frame (new.d, b4lb)
result<-questionr::freq(temp.d$b4lb, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Cirrhosis, liver damage Age")
Cirrhosis, liver damage Age
| 0 |
0 |
0 |
NA |
# Arthritis
b4ma <- as.factor(d[,"b4ma"])
# Make "*" to NA
b4ma[which(b4ma=="*")]<-"NA"
levels(b4ma) <- list(No="1",
Yes="2")
b4ma <- ordered(b4ma, c("No", "Yes"))
new.d <- data.frame(new.d, b4ma)
new.d <- apply_labels(new.d, b4ma = "Arthritis")
temp.d <- data.frame (new.d, b4ma)
result<-questionr::freq(temp.d$b4ma, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Arthritis")
Arthritis
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b4mb <- as.factor(d[,"b4mb"])
new.d <- data.frame(new.d, b4mb)
new.d <- apply_labels(new.d, b4mb = "Arthritis age")
temp.d <- data.frame (new.d, b4mb)
result<-questionr::freq(temp.d$b4mb, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Arthritis Age")
# Dementia
b4na <- as.factor(d[,"b4na"])
# Make "*" to NA
b4na[which(b4na=="*")]<-"NA"
levels(b4na) <- list(No="1",
Yes="2")
b4na <- ordered(b4na, c("No", "Yes"))
new.d <- data.frame(new.d, b4na)
new.d <- apply_labels(new.d, b4na = "Dementia")
temp.d <- data.frame (new.d, b4na)
result<-questionr::freq(temp.d$b4na, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Dementia")
Dementia
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b4nb <- as.factor(d[,"b4nb"])
new.d <- data.frame(new.d, b4nb)
new.d <- apply_labels(new.d, b4nb = "Dementia age")
temp.d <- data.frame (new.d, b4nb)
result<-questionr::freq(temp.d$b4nb, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Dementia Age")
# Depression
b4oa <- as.factor(d[,"b4oa"])
# Make "*" to NA
b4oa[which(b4oa=="*")]<-"NA"
levels(b4oa) <- list(No="1",
Yes="2")
b4oa <- ordered(b4oa, c("No", "Yes"))
new.d <- data.frame(new.d, b4oa)
new.d <- apply_labels(new.d, b4oa = "Depression")
temp.d <- data.frame (new.d, b4oa)
result<-questionr::freq(temp.d$b4oa, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Depression")
Depression
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b4ob <- as.factor(d[,"b4ob"])
new.d <- data.frame(new.d, b4ob)
new.d <- apply_labels(new.d, b4ob = "Depression age")
temp.d <- data.frame (new.d, b4ob)
result<-questionr::freq(temp.d$b4ob, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Depression Age")
# AIDS
b4pa <- as.factor(d[,"b4pa"])
# Make "*" to NA
b4pa[which(b4pa=="*")]<-"NA"
levels(b4pa) <- list(No="1",
Yes="2")
b4pa <- ordered(b4pa, c("No", "Yes"))
new.d <- data.frame(new.d, b4pa)
new.d <- apply_labels(new.d, b4pa = "AIDS")
temp.d <- data.frame (new.d, b4pa)
result<-questionr::freq(temp.d$b4pa, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "AIDS")
AIDS
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b4pb <- as.factor(d[,"b4pb"])
new.d <- data.frame(new.d, b4pb)
new.d <- apply_labels(new.d, b4pb = "AIDS age")
temp.d <- data.frame (new.d, b4pb)
result<-questionr::freq(temp.d$b4pb, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "AIDS Age")
# Other Cancer
b4qa <- as.factor(d[,"b4qa"])
# Make "*" to NA
b4qa[which(b4qa=="*")]<-"NA"
levels(b4qa) <- list(No="1",
Yes="2")
b4qa <- ordered(b4qa, c("No", "Yes"))
new.d <- data.frame(new.d, b4qa)
new.d <- apply_labels(new.d, b4qa = "Other Cancer")
temp.d <- data.frame (new.d, b4qa)
result<-questionr::freq(temp.d$b4qa, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Other Cancer")
Other Cancer
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
b4qb <- as.factor(d[,"b4qb"])
new.d <- data.frame(new.d, b4qb)
new.d <- apply_labels(new.d, b4qb = "Other Cancer age")
temp.d <- data.frame (new.d, b4qb)
result<-questionr::freq(temp.d$b4qb, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "Other Cancer Age")
Other Cancer Age
| 0 |
0 |
0 |
NA |
B4Q Other Cancer
b4qother <- d[,"b4qother"]
new.d <- data.frame(new.d, b4qother)
new.d <- apply_labels(new.d, b4qother = "b4qother")
temp.d <- data.frame (new.d, b4qother)
result<-questionr::freq(temp.d$b4qother, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B4Q Other")
B5: Routine care
- B5. Where do you usually go for routine medical care (seeing a doctor for any reason, not just for cancer care)?
- 1=Community health center or free clinic
- 2=Hospital (not emergency)/ urgent care clinic
- 3=Private doctor’s office
- 4=Emergency room
- 5=Veteran’s Affairs/VA
- 6=Other type of location
b5 <- as.factor(d[,"b5"])
# Make "*" to NA
b5[which(b5=="*")]<-"NA"
levels(b5) <- list(Community_center_free_clinic="1",
Hospital_urgent_care_clinic="2",
Private_Dr_office="3",
ER="4",
VA="5",
Other="6")
b5 <- ordered(b5, c("Community_center_free_clinic", "Hospital_urgent_care_clinic", "Private_Dr_office", "ER","VA","Other"))
new.d <- data.frame(new.d, b5)
new.d <- apply_labels(new.d, b5 = "routine medical care")
temp.d <- data.frame (new.d, b5)
result<-questionr::freq(temp.d$b5 ,total = TRUE)
kable(result, format = "simple", align = 'l')
| Community_center_free_clinic |
0 |
NaN |
NaN |
| Hospital_urgent_care_clinic |
0 |
NaN |
NaN |
| Private_Dr_office |
0 |
NaN |
NaN |
| ER |
0 |
NaN |
NaN |
| VA |
0 |
NaN |
NaN |
| Other |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
B5 Other: Routine care
b5other <- d[,"b5other"]
new.d <- data.frame(new.d, b5other)
new.d <- apply_labels(new.d, b5other = "b5other")
temp.d <- data.frame (new.d, b5other)
result<-questionr::freq(temp.d$b5other, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "B5 Other")
C1: Years lived at current address
- C1. How many years have you lived in your current address?
- 1=Less than 1 year
- 2=1-5 years
- 3=6-10 years
- 4=11-15 years
- 5=16-20 years
- 6=21+ years
c1 <- as.factor(d[,"c1"])
# Make "*" to NA
c1[which(c1=="*")]<-"NA"
levels(c1) <- list(Less_than_1_year="1",
years_1_5="2",
years_6_10="3",
years_11_15="4",
years_16_20="5",
years_21_more="6")
c1 <- ordered(c1, c("Less_than_1_year", "years_1_5", "years_6_10", "years_11_15","years_16_20","years_21_more"))
new.d <- data.frame(new.d, c1)
new.d <- apply_labels(new.d, c1 = "living period")
temp.d <- data.frame (new.d, c1)
result<-questionr::freq(temp.d$c1, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l')
| Less_than_1_year |
0 |
NaN |
NaN |
NaN |
NaN |
| years_1_5 |
0 |
NaN |
NaN |
NaN |
NaN |
| years_6_10 |
0 |
NaN |
NaN |
NaN |
NaN |
| years_11_15 |
0 |
NaN |
NaN |
NaN |
NaN |
| years_16_20 |
0 |
NaN |
NaN |
NaN |
NaN |
| years_21_more |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
C2A: Feel safe walking in the neighborhood
- On average, I felt/feel safe walking in my neighborhood day or night.
- Current (from prostate cancer diagnosis to present)
- Age 31 up to just before prostate cancer diagnosis)
- Childhood or young adult life (up to age 30)
- 1=Strongly Agree
- 2=Agree
- 3=Neutral (neither agree nor disagree)
- 4=Disagree
- 5=Strongly Disagree
c2a1 <- as.factor(d[,"c2a1"])
# Make "*" to NA
c2a1[which(c2a1=="*")]<-"NA"
levels(c2a1) <- list(Strongly_Agree="1",
Agree="2",
Neutral="3",
Disagree="4",
Strongly_Disagree="5")
c2a1 <- ordered(c2a1, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, c2a1)
new.d <- apply_labels(new.d, c2a1 = "walk in the neighborhood-current")
temp.d <- data.frame (new.d, c2a1)
c2a2 <- as.factor(d[,"c2a2"])
# Make "*" to NA
c2a2[which(c2a2=="*")]<-"NA"
levels(c2a2) <- list(Strongly_Agree="1",
Agree="2",
Neutral="3",
Disagree="4",
Strongly_Disagree="5")
c2a2 <- ordered(c2a2, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, c2a2)
new.d <- apply_labels(new.d, c2a2 = "walk in the neighborhood-age 31 up")
temp.d <- data.frame (new.d, c2a2)
c2a3 <- as.factor(d[,"c2a3"])
# Make "*" to NA
c2a3[which(c2a3=="*")]<-"NA"
levels(c2a3) <- list(Strongly_Agree="1",
Agree="2",
Neutral="3",
Disagree="4",
Strongly_Disagree="5")
c2a3 <- ordered(c2a3, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, c2a3)
new.d <- apply_labels(new.d, c2a3 = "walk in the neighborhood-Childhood or young")
temp.d <- data.frame (new.d, c2a3)
result<-questionr::freq(temp.d$c2a1, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
| Strongly_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Neutral |
0 |
NaN |
NaN |
NaN |
NaN |
| Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Strongly_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
result<-questionr::freq(temp.d$c2a2, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis)")
2. Age 31 up to just before prostate cancer diagnosis)
| Strongly_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Neutral |
0 |
NaN |
NaN |
NaN |
NaN |
| Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Strongly_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
result<-questionr::freq(temp.d$c2a3, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Strongly_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Neutral |
0 |
NaN |
NaN |
NaN |
NaN |
| Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Strongly_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
C2B: Violence
- Violence was/is not a problem in my neighborhood.
- Current (from prostate cancer diagnosis to present)
- Age 31 up to just before prostate cancer diagnosis)
- Childhood or young adult life (up to age 30)
- 1=Strongly Agree
- 2=Agree
- 3=Neutral (neither agree nor disagree)
- 4=Disagree
- 5=Strongly Disagree
c2b1 <- as.factor(d[,"c2b1"])
# Make "*" to NA
c2b1[which(c2b1=="*")]<-"NA"
levels(c2b1) <- list(Strongly_Agree="1",
Agree="2",
Neutral="3",
Disagree="4",
Strongly_Disagree="5")
c2b1 <- ordered(c2b1, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, c2b1)
new.d <- apply_labels(new.d, c2b1 = "Violence in the neighborhood-current")
temp.d <- data.frame (new.d, c2b1)
c2b2 <- as.factor(d[,"c2b2"])
# Make "*" to NA
c2b2[which(c2b2=="*")]<-"NA"
levels(c2b2) <- list(Strongly_Agree="1",
Agree="2",
Neutral="3",
Disagree="4",
Strongly_Disagree="5")
c2b2 <- ordered(c2b2, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, c2b2)
new.d <- apply_labels(new.d, c2b2 = "Violence in the neighborhood-age 31 up")
temp.d <- data.frame (new.d, c2b2)
c2b3 <- as.factor(d[,"c2b3"])
# Make "*" to NA
c2b3[which(c2b3=="*")]<-"NA"
levels(c2b3) <- list(Strongly_Agree="1",
Agree="2",
Neutral="3",
Disagree="4",
Strongly_Disagree="5")
c2b3 <- ordered(c2b3, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, c2b3)
new.d <- apply_labels(new.d, c2b3 = "Violence in the neighborhood-Childhood or young")
temp.d <- data.frame (new.d, c2b3)
result<-questionr::freq(temp.d$c2b1, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
| Strongly_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Neutral |
0 |
NaN |
NaN |
NaN |
NaN |
| Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Strongly_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
result<-questionr::freq(temp.d$c2b2, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis)")
2. Age 31 up to just before prostate cancer diagnosis)
| Strongly_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Neutral |
0 |
NaN |
NaN |
NaN |
NaN |
| Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Strongly_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
result<-questionr::freq(temp.d$c2b3, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Strongly_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Neutral |
0 |
NaN |
NaN |
NaN |
NaN |
| Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Strongly_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
C2C: Safe from crime
- My neighborhood was/is safe from crime.
- Current (from prostate cancer diagnosis to present)
- Age 31 up to just before prostate cancer diagnosis)
- Childhood or young adult life (up to age 30)
- 1=Strongly Agree
- 2=Agree
- 3=Neutral (neither agree nor disagree)
- 4=Disagree
- 5=Strongly Disagree
c2c1 <- as.factor(d[,"c2c1"])
# Make "*" to NA
c2c1[which(c2c1=="*")]<-"NA"
levels(c2c1) <- list(Strongly_Agree="1",
Agree="2",
Neutral="3",
Disagree="4",
Strongly_Disagree="5")
c2c1 <- ordered(c2c1, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, c2c1)
new.d <- apply_labels(new.d, c2c1 = "safe from crime in the neighborhood-current")
temp.d <- data.frame (new.d, c2c1)
c2c2 <- as.factor(d[,"c2c2"])
# Make "*" to NA
c2c2[which(c2c2=="*")]<-"NA"
levels(c2c2) <- list(Strongly_Agree="1",
Agree="2",
Neutral="3",
Disagree="4",
Strongly_Disagree="5")
c2c2 <- ordered(c2c2, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, c2c2)
new.d <- apply_labels(new.d, c2c2 = "safe from crime in the neighborhood-age 31 up")
temp.d <- data.frame (new.d, c2c2)
c2c3 <- as.factor(d[,"c2c3"])
# Make "*" to NA
c2c3[which(c2c3=="*")]<-"NA"
levels(c2c3) <- list(Strongly_Agree="1",
Agree="2",
Neutral="3",
Disagree="4",
Strongly_Disagree="5")
c2c3 <- ordered(c2c3, c("Strongly_Agree", "Agree", "Neutral", "Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, c2c3)
new.d <- apply_labels(new.d, c2c3 = "safe from crime in the neighborhood-Childhood or young")
temp.d <- data.frame (new.d, c2c3)
result<-questionr::freq(temp.d$c2c1, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
| Strongly_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Neutral |
0 |
NaN |
NaN |
NaN |
NaN |
| Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Strongly_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
result<-questionr::freq(temp.d$c2c2, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis)")
2. Age 31 up to just before prostate cancer diagnosis)
| Strongly_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Neutral |
0 |
NaN |
NaN |
NaN |
NaN |
| Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Strongly_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
result<-questionr::freq(temp.d$c2c3, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Strongly_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Neutral |
0 |
NaN |
NaN |
NaN |
NaN |
| Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Strongly_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
C3A: Traffic
- C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
- Traffic
- Current (from prostate cancer diagnosis to present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Non/Minor problem
- 2=Somewhat serious problem
- 3=Very serious problem
- 88=Don’t Know
c3a1 <- as.factor(d[,"c3a1"])
# Make "*" to NA
c3a1[which(c3a1=="*")]<-"NA"
levels(c3a1) <- list(Non_Minor="1",
Somewhat_serious="2",
Very_serious="3",
Dont_know="88")
c3a1 <- ordered(c3a1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
new.d <- data.frame(new.d, c3a1)
new.d <- apply_labels(new.d, c3a1 = "A lot of noise-Current")
temp.d <- data.frame (new.d, c3a1)
c3a2 <- as.factor(d[,"c3a2"])
# Make "*" to NA
c3a2[which(c3a2=="*")]<-"NA"
levels(c3a2) <- list(Non_Minor="1",
Somewhat_serious="2",
Very_serious="3",
Dont_know="88")
c3a2 <- ordered(c3a2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
new.d <- data.frame(new.d, c3a2)
new.d <- apply_labels(new.d, c3a2 = "A lot of noise-age 31 up")
temp.d <- data.frame (new.d, c3a2)
c3a3 <- as.factor(d[,"c3a3"])
# Make "*" to NA
c3a3[which(c3a3=="*")]<-"NA"
levels(c3a3) <- list(Non_Minor="1",
Somewhat_serious="2",
Very_serious="3",
Dont_know="88")
c3a3 <- ordered(c3a3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
new.d <- data.frame(new.d, c3a3)
new.d <- apply_labels(new.d, c3a3 = "A lot of noise-Childhood or young")
temp.d <- data.frame (new.d, c3a3)
result<-questionr::freq(temp.d$c3a1, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
| Non_Minor |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_serious |
0 |
NaN |
NaN |
NaN |
NaN |
| Very_serious |
0 |
NaN |
NaN |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
result<-questionr::freq(temp.d$c3a2, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Non_Minor |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_serious |
0 |
NaN |
NaN |
NaN |
NaN |
| Very_serious |
0 |
NaN |
NaN |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
result<-questionr::freq(temp.d$c3a3, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Non_Minor |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_serious |
0 |
NaN |
NaN |
NaN |
NaN |
| Very_serious |
0 |
NaN |
NaN |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
C3B: Noise
- C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
- A lot of noise
- Current (from prostate cancer diagnosis to present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Non/Minor problem
- 2=Somewhat serious problem
- 3=Very serious problem
- 88=Don’t Know
c3b1 <- as.factor(d[,"c3b1"])
# Make "*" to NA
c3b1[which(c3b1=="*")]<-"NA"
levels(c3b1) <- list(Non_Minor="1",
Somewhat_serious="2",
Very_serious="3",
Dont_know="88")
c3b1 <- ordered(c3b1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
new.d <- data.frame(new.d, c3b1)
new.d <- apply_labels(new.d, c3b1 = "A lot of noise-Current")
temp.d <- data.frame (new.d, c3b1)
c3b2 <- as.factor(d[,"c3b2"])
# Make "*" to NA
c3b2[which(c3b2=="*")]<-"NA"
levels(c3b2) <- list(Non_Minor="1",
Somewhat_serious="2",
Very_serious="3",
Dont_know="88")
c3b2 <- ordered(c3b2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
new.d <- data.frame(new.d, c3b2)
new.d <- apply_labels(new.d, c3b2 = "A lot of noise-age 31 up")
temp.d <- data.frame (new.d, c3b2)
c3b3 <- as.factor(d[,"c3b3"])
# Make "*" to NA
c3b3[which(c3b3=="*")]<-"NA"
levels(c3b3) <- list(Non_Minor="1",
Somewhat_serious="2",
Very_serious="3",
Dont_know="88")
c3b3 <- ordered(c3b3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
new.d <- data.frame(new.d, c3b3)
new.d <- apply_labels(new.d, c3b3 = "A lot of noise-Childhood or young")
temp.d <- data.frame (new.d, c3b3)
result<-questionr::freq(temp.d$c3b1, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
| Non_Minor |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_serious |
0 |
NaN |
NaN |
NaN |
NaN |
| Very_serious |
0 |
NaN |
NaN |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
result<-questionr::freq(temp.d$c3b2, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Non_Minor |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_serious |
0 |
NaN |
NaN |
NaN |
NaN |
| Very_serious |
0 |
NaN |
NaN |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
result<-questionr::freq(temp.d$c3b3, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Non_Minor |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_serious |
0 |
NaN |
NaN |
NaN |
NaN |
| Very_serious |
0 |
NaN |
NaN |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
C3C: Trash and litter
- C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
- Trash and litter
- Current (from prostate cancer diagnosis to present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Non/Minor problem
- 2=Somewhat serious problem
- 3=Very serious problem
- 88=Don’t Know
c3c1 <- as.factor(d[,"c3c1"])
# Make "*" to NA
c3c1[which(c3c1=="*")]<-"NA"
levels(c3c1) <- list(Non_Minor="1",
Somewhat_serious="2",
Very_serious="3",
Dont_know="88")
c3c1 <- ordered(c3c1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
new.d <- data.frame(new.d, c3c1)
new.d <- apply_labels(new.d, c3c1 = "Trash and litter-Current")
temp.d <- data.frame (new.d, c3c1)
c3c2 <- as.factor(d[,"c3c2"])
# Make "*" to NA
c3c2[which(c3c2=="*")]<-"NA"
levels(c3c2) <- list(Non_Minor="1",
Somewhat_serious="2",
Very_serious="3",
Dont_know="88")
c3c2 <- ordered(c3c2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
new.d <- data.frame(new.d, c3c2)
new.d <- apply_labels(new.d, c3c2 = "Trash and litter-age 31 up")
temp.d <- data.frame (new.d, c3c2)
c3c3 <- as.factor(d[,"c3c3"])
# Make "*" to NA
c3c3[which(c3c3=="*")]<-"NA"
levels(c3c3) <- list(Non_Minor="1",
Somewhat_serious="2",
Very_serious="3",
Dont_know="88")
c3c3 <- ordered(c3c3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
new.d <- data.frame(new.d, c3c3)
new.d <- apply_labels(new.d, c3c3 = "Trash and litter-Childhood or young")
temp.d <- data.frame (new.d, c3c3)
result<-questionr::freq(temp.d$c3c1, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
| Non_Minor |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_serious |
0 |
NaN |
NaN |
NaN |
NaN |
| Very_serious |
0 |
NaN |
NaN |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
result<-questionr::freq(temp.d$c3c2, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Non_Minor |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_serious |
0 |
NaN |
NaN |
NaN |
NaN |
| Very_serious |
0 |
NaN |
NaN |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
result<-questionr::freq(temp.d$c3c3, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Non_Minor |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_serious |
0 |
NaN |
NaN |
NaN |
NaN |
| Very_serious |
0 |
NaN |
NaN |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
C3D: Too much light at night
- C3. Thinking about your neighborhood during the following 3 time periods, as a whole, how much of a problem is/was…
- Too much light at night
- Current (from prostate cancer diagnosis to present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Non/Minor problem
- 2=Somewhat serious problem
- 3=Very serious problem
- 88=Don’t Know
c3d1 <- as.factor(d[,"c3d1"])
# Make "*" to NA
c3d1[which(c3d1=="*")]<-"NA"
levels(c3d1) <- list(Non_Minor="1",
Somewhat_serious="2",
Very_serious="3",
Dont_know="88")
c3d1 <- ordered(c3d1, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
new.d <- data.frame(new.d, c3d1)
new.d <- apply_labels(new.d, c3d1 = "Too much light at night-Current")
temp.d <- data.frame (new.d, c3d1)
c3d2 <- as.factor(d[,"c3d2"])
# Make "*" to NA
c3d2[which(c3d2=="*")]<-"NA"
levels(c3d2) <- list(Non_Minor="1",
Somewhat_serious="2",
Very_serious="3",
Dont_know="88")
c3d2 <- ordered(c3d2, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
new.d <- data.frame(new.d, c3d2)
new.d <- apply_labels(new.d, c3d2 = "Too much light at night-age 31 up")
temp.d <- data.frame (new.d, c3d2)
c3d3 <- as.factor(d[,"c3d3"])
# Make "*" to NA
c3d3[which(c3d3=="*")]<-"NA"
levels(c3d3) <- list(Non_Minor="1",
Somewhat_serious="2",
Very_serious="3",
Dont_know="88")
c3d3 <- ordered(c3d3, c("Non_Minor", "Somewhat_serious", "Very_serious", "Dont_know"))
new.d <- data.frame(new.d, c3d3)
new.d <- apply_labels(new.d, c3d3 = "Too much light at night-Childhood or young")
temp.d <- data.frame (new.d, c3d3)
result<-questionr::freq(temp.d$c3d1, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
| Non_Minor |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_serious |
0 |
NaN |
NaN |
NaN |
NaN |
| Very_serious |
0 |
NaN |
NaN |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
result<-questionr::freq(temp.d$c3d2, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Non_Minor |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_serious |
0 |
NaN |
NaN |
NaN |
NaN |
| Very_serious |
0 |
NaN |
NaN |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
result<-questionr::freq(temp.d$c3d3, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Non_Minor |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_serious |
0 |
NaN |
NaN |
NaN |
NaN |
| Very_serious |
0 |
NaN |
NaN |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
C4A: Neighbors talking outside
- C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
- How often do/did you see neighbors talking outside in the yard, on the street, at the corner park, etc.?
- Current (from prostate cancer diagnosis to present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Often
- 2=Sometimes
- 3=Rarely/Never
- 88=Don’t Know
c4a1 <- as.factor(d[,"c4a1"])
# Make "*" to NA
c4a1[which(c4a1=="*")]<-"NA"
levels(c4a1) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4a1 <- ordered(c4a1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4a1)
new.d <- apply_labels(new.d, c4a1 = "Talk outside-Current")
temp.d <- data.frame (new.d, c4a1)
c4a2 <- as.factor(d[,"c4a2"])
# Make "*" to NA
c4a2[which(c4a2=="*")]<-"NA"
levels(c4a2) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4a2 <- ordered(c4a2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4a2)
new.d <- apply_labels(new.d, c4a2 = "Talk outside-age 31 up")
temp.d <- data.frame (new.d, c4a2)
c4a3 <- as.factor(d[,"c4a3"])
# Make "*" to NA
c4a3[which(c4a3=="*")]<-"NA"
levels(c4a3) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4a3 <- ordered(c4a3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4a3)
new.d <- apply_labels(new.d, c4a3 = "Talk outside-Childhood or young")
temp.d <- data.frame (new.d, c4a3)
result<-questionr::freq(temp.d$c4a1, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
| Often |
0 |
NaN |
NaN |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
NaN |
NaN |
| Rarely_Never |
0 |
NaN |
NaN |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
result<-questionr::freq(temp.d$c4a2, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Often |
0 |
NaN |
NaN |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
NaN |
NaN |
| Rarely_Never |
0 |
NaN |
NaN |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
result<-questionr::freq(temp.d$c4a3, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Often |
0 |
NaN |
NaN |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
NaN |
NaN |
| Rarely_Never |
0 |
NaN |
NaN |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
C4B: Neighbors watch out for each other
- C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
- How often do/did neighbors watch out for each other, such as calling if they see a problem?
- Current (from prostate cancer diagnosis to present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Often
- 2=Sometimes
- 3=Rarely/Never
- 88=Don’t Know
c4b1 <- as.factor(d[,"c4b1"])
# Make "*" to NA
c4b1[which(c4b1=="*")]<-"NA"
levels(c4b1) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4b1 <- ordered(c4b1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4b1)
new.d <- apply_labels(new.d, c4b1 = "watch out-Current")
temp.d <- data.frame (new.d, c4b1)
c4b2 <- as.factor(d[,"c4b2"])
# Make "*" to NA
c4b2[which(c4b2=="*")]<-"NA"
levels(c4b2) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4b2 <- ordered(c4b2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4b2)
new.d <- apply_labels(new.d, c4b2 = "watch out-age 31 up")
temp.d <- data.frame (new.d, c4b2)
c4b3 <- as.factor(d[,"c4b3"])
# Make "*" to NA
c4b3[which(c4b3=="*")]<-"NA"
levels(c4b3) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4b3 <- ordered(c4b3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4b3)
new.d <- apply_labels(new.d, c4b3 = "watch out-Childhood or young")
temp.d <- data.frame (new.d, c4b3)
result<-questionr::freq(temp.d$c4b1, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
| Often |
0 |
NaN |
NaN |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
NaN |
NaN |
| Rarely_Never |
0 |
NaN |
NaN |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
result<-questionr::freq(temp.d$c4b2, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Often |
0 |
NaN |
NaN |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
NaN |
NaN |
| Rarely_Never |
0 |
NaN |
NaN |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
result<-questionr::freq(temp.d$c4b3, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Often |
0 |
NaN |
NaN |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
NaN |
NaN |
| Rarely_Never |
0 |
NaN |
NaN |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
C4C: Neighbors know by name
- C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
- How many neighbors do/did you know by name?
- Current (from prostate cancer diagnosis to present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Often
- 2=Sometimes
- 3=Rarely/Never
- 88=Don’t Know
c4c1 <- as.factor(d[,"c4c1"])
# Make "*" to NA
c4c1[which(c4c1=="*")]<-"NA"
levels(c4c1) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4c1 <- ordered(c4c1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4c1)
new.d <- apply_labels(new.d, c4c1 = "Know names-Current")
temp.d <- data.frame (new.d, c4c1)
c4c2 <- as.factor(d[,"c4c2"])
# Make "*" to NA
c4c2[which(c4c2=="*")]<-"NA"
levels(c4c2) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4c2 <- ordered(c4c2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4c2)
new.d <- apply_labels(new.d, c4c2 = "Know names-age 31 up")
temp.d <- data.frame (new.d, c4c2)
c4c3 <- as.factor(d[,"c4c3"])
# Make "*" to NA
c4c3[which(c4c3=="*")]<-"NA"
levels(c4c3) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4c3 <- ordered(c4c3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4c3)
new.d <- apply_labels(new.d, c4c3 = "Know names-Childhood or young")
temp.d <- data.frame (new.d, c4c3)
result<-questionr::freq(temp.d$c4c1, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
| Often |
0 |
NaN |
NaN |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
NaN |
NaN |
| Rarely_Never |
0 |
NaN |
NaN |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
result<-questionr::freq(temp.d$c4c2, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Often |
0 |
NaN |
NaN |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
NaN |
NaN |
| Rarely_Never |
0 |
NaN |
NaN |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
result<-questionr::freq(temp.d$c4c3, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Often |
0 |
NaN |
NaN |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
NaN |
NaN |
| Rarely_Never |
0 |
NaN |
NaN |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
C4D: Friendly talks with neighbors
- C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
- How many neighbors do/did you have a friendly talk with at least once a week?
- Current (from prostate cancer diagnosis to present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Often
- 2=Sometimes
- 3=Rarely/Never
- 88=Don’t Know
c4d1 <- as.factor(d[,"c4d1"])
# Make "*" to NA
c4d1[which(c4d1=="*")]<-"NA"
levels(c4d1) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4d1 <- ordered(c4d1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4d1)
new.d <- apply_labels(new.d, c4d1 = "Know names-Current")
temp.d <- data.frame (new.d, c4d1)
c4d2 <- as.factor(d[,"c4d2"])
# Make "*" to NA
c4d2[which(c4d2=="*")]<-"NA"
levels(c4d2) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4d2 <- ordered(c4d2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4d2)
new.d <- apply_labels(new.d, c4d2 = "Know names-age 31 up")
temp.d <- data.frame (new.d, c4d2)
c4d3 <- as.factor(d[,"c4d3"])
# Make "*" to NA
c4d3[which(c4d3=="*")]<-"NA"
levels(c4d3) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4d3 <- ordered(c4d3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4d3)
new.d <- apply_labels(new.d, c4d3 = "Know names-Childhood or young")
temp.d <- data.frame (new.d, c4d3)
result<-questionr::freq(temp.d$c4d1, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
| Often |
0 |
NaN |
NaN |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
NaN |
NaN |
| Rarely_Never |
0 |
NaN |
NaN |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
result<-questionr::freq(temp.d$c4d2, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Often |
0 |
NaN |
NaN |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
NaN |
NaN |
| Rarely_Never |
0 |
NaN |
NaN |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
result<-questionr::freq(temp.d$c4d3, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Often |
0 |
NaN |
NaN |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
NaN |
NaN |
| Rarely_Never |
0 |
NaN |
NaN |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
C4E: Ask neighbors for help
- C4. Thinking about your NEIGHBORS, as a whole, during the following 3 time periods:
- How many neighbors could you ask for help, such as to “borrow a cup of sugar” or some other small favor?
- Current (from prostate cancer diagnosis to present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Often
- 2=Sometimes
- 3=Rarely/Never
- 88=Don’t Know
c4e1 <- as.factor(d[,"c4e1"])
# Make "*" to NA
c4e1[which(c4e1=="*")]<-"NA"
levels(c4e1) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4e1 <- ordered(c4e1, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4e1)
new.d <- apply_labels(new.d, c4e1 = "ask for help-Current")
temp.d <- data.frame (new.d, c4e1)
c4e2 <- as.factor(d[,"c4e2"])
# Make "*" to NA
c4e2[which(c4e2=="*")]<-"NA"
levels(c4e2) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4e2 <- ordered(c4e2, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4e2)
new.d <- apply_labels(new.d, c4e2 = "ask for help-age 31 up")
temp.d <- data.frame (new.d, c4e2)
c4e3 <- as.factor(d[,"c4e3"])
# Make "*" to NA
c4e3[which(c4e3=="*")]<-"NA"
levels(c4e3) <- list(Often="1",
Sometimes="2",
Rarely_Never="3",
Dont_know="88")
c4e3 <- ordered(c4e3, c("Often", "Sometimes", "Rarely_Never", "Dont_know"))
new.d <- data.frame(new.d, c4e3)
new.d <- apply_labels(new.d, c4e3 = "ask for help-Childhood or young")
temp.d <- data.frame (new.d, c4e3)
result<-questionr::freq(temp.d$c4e1, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "1. Current (from prostate cancer diagnosis to present)")
1. Current (from prostate cancer diagnosis to present)
| Often |
0 |
NaN |
NaN |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
NaN |
NaN |
| Rarely_Never |
0 |
NaN |
NaN |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
result<-questionr::freq(temp.d$c4e2, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Often |
0 |
NaN |
NaN |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
NaN |
NaN |
| Rarely_Never |
0 |
NaN |
NaN |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
result<-questionr::freq(temp.d$c4e3, cum = TRUE ,total = TRUE)
kable(result, format = "simple", align = 'l',caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Often |
0 |
NaN |
NaN |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
NaN |
NaN |
| Rarely_Never |
0 |
NaN |
NaN |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
D1: Treat you because of your race/ethnicity
- D1. In the following questions, we are interested in your perceptions about the way other people have treated you because of your race/ethnicity or skin color.
- At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
- For unfair reasons, have you ever not been hired for a job?
- Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
- Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
- Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
- Have you ever been unfairly denied a bank loan?
- Have you ever been unfairly treated when getting medical care?
- If yes, How stressful was this experience?
- 1=Not at all
- 2=A little
- 3=Somewhat
- 4=Extremely
# a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
d1aa <- as.factor(d[,"d1aa"])
# Make "*" to NA
d1aa[which(d1aa=="*")]<-"NA"
levels(d1aa) <- list(No="1",
Yes="2")
d1aa <- ordered(d1aa, c("No","Yes"))
new.d <- data.frame(new.d, d1aa)
new.d <- apply_labels(new.d, d1aa = "fired or denied a promotion")
temp.d <- data.frame (new.d, d1aa)
d1ab <- as.factor(d[,"d1ab"])
# Make "*" to NA
d1ab[which(d1ab=="*")]<-"NA"
levels(d1ab) <- list(Not_at_all="1",
A_little="2",
Somewhat="3",
Extremely="4")
d1ab <- ordered(d1ab, c("No","Yes"))
new.d <- data.frame(new.d, d1ab)
new.d <- apply_labels(new.d, d1ab = "fired or denied a promotion-stressful")
temp.d <- data.frame (new.d, d1ab)
result<-questionr::freq(temp.d$d1aa,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
")
a. At any time in your life, have you ever been unfairly fired from a job or been unfairly denied a promotion?
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
result<-questionr::freq(temp.d$d1ab,total = TRUE,cum=TRUE)
kable(result, format = "simple", align = 'l', caption = "a. If yes, How stressful was this experience?")
a. If yes, How stressful was this experience?
| No |
0 |
NaN |
NaN |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
# b. For unfair reasons, have you ever not been hired for a job?
d1ba <- as.factor(d[,"d1ba"])
# Make "*" to NA
d1ba[which(d1ba=="*")]<-"NA"
levels(d1ba) <- list(No="1",
Yes="2")
d1ba <- ordered(d1ba, c("No","Yes"))
new.d <- data.frame(new.d, d1ba)
new.d <- apply_labels(new.d, d1ba = "not be hired")
temp.d <- data.frame (new.d, d1ba)
d1bb <- as.factor(d[,"d1bb"])
# Make "*" to NA
d1bb[which(d1bb=="*")]<-"NA"
levels(d1bb) <- list(Not_at_all="1",
A_little="2",
Somewhat="3",
Extremely="4")
d1bb <- ordered(d1bb, c("No","Yes"))
new.d <- data.frame(new.d, d1bb)
new.d <- apply_labels(new.d, d1bb = "not be hired-stressful")
temp.d <- data.frame (new.d, d1bb)
result<-questionr::freq(temp.d$d1ba,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "b. For unfair reasons, have you ever not been hired for a job?")
b. For unfair reasons, have you ever not been hired for a job?
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
result<-questionr::freq(temp.d$d1bb,total = TRUE,cum=TRUE)
kable(result, format = "simple", align = 'l', caption = "b. If yes, How stressful was this experience?")
b. If yes, How stressful was this experience?
| No |
0 |
NaN |
NaN |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
# c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
d1ca <- as.factor(d[,"d1ca"])
# Make "*" to NA
d1ca[which(d1ca=="*")]<-"NA"
levels(d1ca) <- list(No="1",
Yes="2")
d1ca <- ordered(d1ca, c( "No","Yes"))
new.d <- data.frame(new.d, d1ca)
new.d <- apply_labels(new.d, d1ca = "By police")
temp.d <- data.frame (new.d, d1ca)
result<-questionr::freq(temp.d$d1ca,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?")
c. Have you ever been unfairly stopped, searched, questioned, physically threatened or abused by the police?
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
d1cb <- as.factor(d[,"d1cb"])
# Make "*" to NA
d1cb[which(d1cb=="*")]<-"NA"
levels(d1cb) <- list(Not_at_all="1",
A_little="2",
Somewhat="3",
Extremely="4")
d1cb <- ordered(d1cb, c("No","Yes"))
new.d <- data.frame(new.d, d1cb)
new.d <- apply_labels(new.d, d1cb = "By police-stressful")
temp.d <- data.frame (new.d, d1cb)
result<-questionr::freq(temp.d$d1cb,total = TRUE,cum=TRUE)
kable(result, format = "simple", align = 'l', caption = "c. If yes, How stressful was this experience?")
c. If yes, How stressful was this experience?
| No |
0 |
NaN |
NaN |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
# d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
d1da <- as.factor(d[,"d1da"])
# Make "*" to NA
d1da[which(d1da=="*")]<-"NA"
levels(d1da) <- list(No="1",
Yes="2")
d1da <- ordered(d1da, c( "No","Yes"))
new.d <- data.frame(new.d, d1da)
new.d <- apply_labels(new.d, d1da = "unfair education")
temp.d <- data.frame (new.d, d1da)
result<-questionr::freq(temp.d$d1da,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?")
d. Have you ever been unfairly discouraged by a teacher or advisor from continuing your education?
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
d1db <- as.factor(d[,"d1db"])
# Make "*" to NA
d1db[which(d1db=="*")]<-"NA"
levels(d1db) <- list(Not_at_all="1",
A_little="2",
Somewhat="3",
Extremely="4")
d1db <- ordered(d1db, c("No","Yes"))
new.d <- data.frame(new.d, d1db)
new.d <- apply_labels(new.d, d1db = "unfair education-stressful")
temp.d <- data.frame (new.d, d1db)
result<-questionr::freq(temp.d$d1db,total = TRUE,cum=TRUE)
kable(result, format = "simple", align = 'l', caption = "d. If yes, How stressful was this experience?")
d. If yes, How stressful was this experience?
| No |
0 |
NaN |
NaN |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
# e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
d1ea <- as.factor(d[,"d1ea"])
# Make "*" to NA
d1ea[which(d1ea=="*")]<-"NA"
levels(d1ea) <- list(No="1",
Yes="2")
d1ea <- ordered(d1ea, c("No","Yes"))
new.d <- data.frame(new.d, d1ea)
new.d <- apply_labels(new.d, d1ea = "refuse to sell or rent")
temp.d <- data.frame (new.d, d1ea)
result<-questionr::freq(temp.d$d1ea,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?")
e. Have you ever been unfairly prevented from moving into a neighborhood because the landlord or a realtor refused to sell or rent you a house or apartment?
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
d1eb <- as.factor(d[,"d1eb"])
# Make "*" to NA
d1eb[which(d1eb=="*")]<-"NA"
levels(d1eb) <- list(Not_at_all="1",
A_little="2",
Somewhat="3",
Extremely="4")
d1eb <- ordered(d1eb, c("No","Yes"))
new.d <- data.frame(new.d, d1eb)
new.d <- apply_labels(new.d, d1eb = "refuse to sell or rent-stressful")
temp.d <- data.frame (new.d, d1eb)
result<-questionr::freq(temp.d$d1eb,total = TRUE,cum=TRUE)
kable(result, format = "simple", align = 'l', caption = "e. If yes, How stressful was this experience?")
e. If yes, How stressful was this experience?
| No |
0 |
NaN |
NaN |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
# f. Have you ever been unfairly denied a bank loan?
d1fa <- as.factor(d[,"d1fa"])
# Make "*" to NA
d1fa[which(d1fa=="*")]<-"NA"
levels(d1fa) <- list(No="1",
Yes="2")
d1fa <- ordered(d1fa, c("No","Yes"))
new.d <- data.frame(new.d, d1fa)
new.d <- apply_labels(new.d, d1fa = "Bank loan")
temp.d <- data.frame (new.d, d1fa)
result<-questionr::freq(temp.d$d1fa,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "f. Have you ever been unfairly denied a bank loan?")
f. Have you ever been unfairly denied a bank loan?
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
d1fb <- as.factor(d[,"d1fb"])
# Make "*" to NA
d1fb[which(d1fb=="*")]<-"NA"
levels(d1fb) <- list(Not_at_all="1",
A_little="2",
Somewhat="3",
Extremely="4")
d1fb <- ordered(d1fb, c("No","Yes"))
new.d <- data.frame(new.d, d1fb)
new.d <- apply_labels(new.d, d1fb = "Bank loan-stressful")
temp.d <- data.frame (new.d, d1fb)
result<-questionr::freq(temp.d$d1fb,total = TRUE,cum=TRUE)
kable(result, format = "simple", align = 'l', caption = "f. If yes, How stressful was this experience?")
f. If yes, How stressful was this experience?
| No |
0 |
NaN |
NaN |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
# g. Have you ever been unfairly treated when getting medical care?
d1ga <- as.factor(d[,"d1ga"])
# Make "*" to NA
d1ga[which(d1ga=="*")]<-"NA"
levels(d1ga) <- list(No="1",
Yes="2")
d1ga <- ordered(d1ga, c("No","Yes"))
new.d <- data.frame(new.d, d1ga)
new.d <- apply_labels(new.d, d1ga = "unfair medical care")
temp.d <- data.frame (new.d, d1ga)
result<-questionr::freq(temp.d$d1ga,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "g. Have you ever been unfairly treated when getting medical care?")
g. Have you ever been unfairly treated when getting medical care?
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
d1gb <- as.factor(d[,"d1gb"])
# Make "*" to NA
d1gb[which(d1gb=="*")]<-"NA"
levels(d1gb) <- list(Not_at_all="1",
A_little="2",
Somewhat="3",
Extremely="4")
d1gb <- ordered(d1gb, c("No","Yes"))
new.d <- data.frame(new.d, d1gb)
new.d <- apply_labels(new.d, d1gb = "unfair medical care-stressful")
temp.d <- data.frame (new.d, d1gb)
result<-questionr::freq(temp.d$d1gb,total = TRUE,cum=TRUE)
kable(result, format = "simple", align = 'l', caption = "g. If yes, How stressful was this experience?")
g. If yes, How stressful was this experience?
| No |
0 |
NaN |
NaN |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
D2: Medical Mistrust
- D2. These next questions are about your current feelings or perceptions regarding healthcare organizations (places where you might get healthcare, like a hospital or clinic). Indicate your level of agreement or disagreement with each statement.
# a. Patients have sometimes been deceived or misled at hospitals.
d2a <- as.factor(d[,"d2a"])
# Make "*" to NA
d2a[which(d2a=="*")]<-"NA"
levels(d2a) <- list(Strongly_Agree="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d2a <- ordered(d2a, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d2a)
new.d <- apply_labels(new.d, d2a = "deceived or misled")
temp.d <- data.frame (new.d, d2a)
result<-questionr::freq(temp.d$d2a,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "a. Patients have sometimes been deceived or misled at hospitals.")
a. Patients have sometimes been deceived or misled at hospitals.
| Strongly_Agree |
0 |
NaN |
NaN |
| Somewhat_Agree |
0 |
NaN |
NaN |
| Somewhat_Disagree |
0 |
NaN |
NaN |
| Strongly_Disagree |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
# b. Hospitals often want to know more about your personal affairs or business than they really need to know.
d2b <- as.factor(d[,"d2b"])
# Make "*" to NA
d2b[which(d2b=="*")]<-"NA"
levels(d2b) <- list(Strongly_Agree="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d2b <- ordered(d2b, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d2b)
new.d <- apply_labels(new.d, d2b = "personal affairs")
temp.d <- data.frame (new.d, d2b)
result<-questionr::freq(temp.d$d2b,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "b. Hospitals often want to know more about your personal affairs or business than they really need to know.")
b. Hospitals often want to know more about your personal affairs or business than they really need to know.
| Strongly_Agree |
0 |
NaN |
NaN |
| Somewhat_Agree |
0 |
NaN |
NaN |
| Somewhat_Disagree |
0 |
NaN |
NaN |
| Strongly_Disagree |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
# c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
d2c <- as.factor(d[,"d2c"])
# Make "*" to NA
d2c[which(d2c=="*")]<-"NA"
levels(d2c) <- list(Strongly_Agree="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d2c <- ordered(d2c, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d2c)
new.d <- apply_labels(new.d, d2c = "harmful experiments")
temp.d <- data.frame (new.d, d2c)
result<-questionr::freq(temp.d$d2c,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "c. Hospitals have sometimes done harmful experiments on patients without their knowledge.")
c. Hospitals have sometimes done harmful experiments on patients without their knowledge.
| Strongly_Agree |
0 |
NaN |
NaN |
| Somewhat_Agree |
0 |
NaN |
NaN |
| Somewhat_Disagree |
0 |
NaN |
NaN |
| Strongly_Disagree |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
# d. Rich patients receive better care at hospitals than poor patients.
d2d <- as.factor(d[,"d2d"])
# Make "*" to NA
d2d[which(d2d=="*")]<-"NA"
levels(d2d) <- list(Strongly_Agree="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d2d <- ordered(d2d, c( "Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d2d)
new.d <- apply_labels(new.d, d2d = "Rich patients better care")
temp.d <- data.frame (new.d, d2d)
result<-questionr::freq(temp.d$d2d,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "d. Rich patients receive better care at hospitals than poor patients.")
d. Rich patients receive better care at hospitals than poor patients.
| Strongly_Agree |
0 |
NaN |
NaN |
| Somewhat_Agree |
0 |
NaN |
NaN |
| Somewhat_Disagree |
0 |
NaN |
NaN |
| Strongly_Disagree |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
# e. Male patients receive better care at hospitals than female patients.
d2e <- as.factor(d[,"d2e"])
# Make "*" to NA
d2e[which(d2e=="*")]<-"NA"
levels(d2e) <- list(Strongly_Agree="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d2e <- ordered(d2e, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d2e)
new.d <- apply_labels(new.d, d2e = "Male patients better care")
temp.d <- data.frame (new.d, d2e)
result<-questionr::freq(temp.d$d2e,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "e. Male patients receive better care at hospitals than female patients.")
e. Male patients receive better care at hospitals than female patients.
| Strongly_Agree |
0 |
NaN |
NaN |
| Somewhat_Agree |
0 |
NaN |
NaN |
| Somewhat_Disagree |
0 |
NaN |
NaN |
| Strongly_Disagree |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
D3A: Treated with less respect
- D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
- You have been treated with less respect than other people
- Current (from prostate cancer diagnosis to the present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Never
- 2=Rarely
- 3=Sometimes
- 4=Often
# 1
d3a1 <- as.factor(d[,"d3a1"])
# Make "*" to NA
d3a1[which(d3a1=="*")]<-"NA"
levels(d3a1) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3a1 <- ordered(d3a1, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3a1)
new.d <- apply_labels(new.d, d3a1 = "less respect-current")
temp.d <- data.frame (new.d, d3a1)
result<-questionr::freq(temp.d$d3a1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#2
d3a2 <- as.factor(d[,"d3a2"])
# Make "*" to NA
d3a2[which(d3a2=="*")]<-"NA"
levels(d3a2) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3a2 <- ordered(d3a2, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3a2)
new.d <- apply_labels(new.d, d3a2 = "less respect-31 up")
temp.d <- data.frame (new.d, d3a2)
result<-questionr::freq(temp.d$d3a2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#3
d3a3 <- as.factor(d[,"d3a3"])
# Make "*" to NA
d3a3[which(d3a3=="*")]<-"NA"
levels(d3a3) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3a3 <- ordered(d3a3, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3a3)
new.d <- apply_labels(new.d, d3a3 = "less respect-child or young")
temp.d <- data.frame (new.d, d3a3)
result<-questionr::freq(temp.d$d3a3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
D3B: Received poorer service
- D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
- You have received poorer service than other people at restaurants or stores
- Current (from prostate cancer diagnosis to the present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Never
- 2=Rarely
- 3=Sometimes
- 4=Often
# 1
d3b1 <- as.factor(d[,"d3b1"])
# Make "*" to NA
d3b1[which(d3b1=="*")]<-"NA"
levels(d3b1) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3b1 <- ordered(d3b1, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3b1)
new.d <- apply_labels(new.d, d3b1 = "poorer service-current")
temp.d <- data.frame (new.d, d3b1)
result<-questionr::freq(temp.d$d3b1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#2
d3b2 <- as.factor(d[,"d3b2"])
# Make "*" to NA
d3b2[which(d3b2=="*")]<-"NA"
levels(d3b2) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3b2 <- ordered(d3b2, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3b2)
new.d <- apply_labels(new.d, d3b2 = "poorer service-31 up")
temp.d <- data.frame (new.d, d3b2)
result<-questionr::freq(temp.d$d3b2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#3
d3b3 <- as.factor(d[,"d3b3"])
# Make "*" to NA
d3b3[which(d3b3=="*")]<-"NA"
levels(d3b3) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3b3 <- ordered(d3b3, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3b3)
new.d <- apply_labels(new.d, d3b3 = "poorer service-child or young")
temp.d <- data.frame (new.d, d3b3)
result<-questionr::freq(temp.d$d3b3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
D3C: Think you are not smart
- D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
- People have acted as if they think you are not smart
- Current (from prostate cancer diagnosis to the present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Never
- 2=Rarely
- 3=Sometimes
- 4=Often
# 1
d3c1 <- as.factor(d[,"d3c1"])
# Make "*" to NA
d3c1[which(d3c1=="*")]<-"NA"
levels(d3c1) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3c1 <- ordered(d3c1, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3c1)
new.d <- apply_labels(new.d, d3c1 = "think you are not smart-current")
temp.d <- data.frame (new.d, d3c1)
result<-questionr::freq(temp.d$d3c1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#2
d3c2 <- as.factor(d[,"d3c2"])
# Make "*" to NA
d3c2[which(d3c2=="*")]<-"NA"
levels(d3c2) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3c2 <- ordered(d3c2, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3c2)
new.d <- apply_labels(new.d, d3c2 = "think you are not smart-31 up")
temp.d <- data.frame (new.d, d3c2)
result<-questionr::freq(temp.d$d3c2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#3
d3c3 <- as.factor(d[,"d3c3"])
# Make "*" to NA
d3c3[which(d3c3=="*")]<-"NA"
levels(d3c3) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3c3 <- ordered(d3c3, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3c3)
new.d <- apply_labels(new.d, d3c3 = "think you are not smart-child or young")
temp.d <- data.frame (new.d, d3c3)
result<-questionr::freq(temp.d$d3c3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
D3D: Be afraid of you
- D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
- People have acted as if they are afraid of you
- Current (from prostate cancer diagnosis to the present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Never
- 2=Rarely
- 3=Sometimes
- 4=Often
# 1
d3d1 <- as.factor(d[,"d3d1"])
# Make "*" to NA
d3d1[which(d3d1=="*")]<-"NA"
levels(d3d1) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3d1 <- ordered(d3d1, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3d1)
new.d <- apply_labels(new.d, d3d1 = "be afraid of you-current")
temp.d <- data.frame (new.d, d3d1)
result<-questionr::freq(temp.d$d3d1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#2
d3d2 <- as.factor(d[,"d3d2"])
# Make "*" to NA
d3d2[which(d3d2=="*")]<-"NA"
levels(d3d2) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3d2 <- ordered(d3d2, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3d2)
new.d <- apply_labels(new.d, d3d2 = "be afraid of you-31 up")
temp.d <- data.frame (new.d, d3d2)
result<-questionr::freq(temp.d$d3d2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#3
d3d3 <- as.factor(d[,"d3d3"])
# Make "*" to NA
d3d3[which(d3d3=="*")]<-"NA"
levels(d3d3) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3d3 <- ordered(d3d3, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3d3)
new.d <- apply_labels(new.d, d3d3 = "be afraid of you-child or young")
temp.d <- data.frame (new.d, d3d3)
result<-questionr::freq(temp.d$d3d3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
D3E: Think you are dishonest
- D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
- People have acted as if they think you are dishonest
- Current (from prostate cancer diagnosis to the present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Never
- 2=Rarely
- 3=Sometimes
- 4=Often
# 1
d3e1 <- as.factor(d[,"d3e1"])
# Make "*" to NA
d3e1[which(d3e1=="*")]<-"NA"
levels(d3e1) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3e1 <- ordered(d3e1, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3e1)
new.d <- apply_labels(new.d, d3e1 = "think you are dishonest-current")
temp.d <- data.frame (new.d, d3e1)
result<-questionr::freq(temp.d$d3e1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#2
d3e2 <- as.factor(d[,"d3e2"])
# Make "*" to NA
d3e2[which(d3e2=="*")]<-"NA"
levels(d3e2) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3e2 <- ordered(d3e2, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3e2)
new.d <- apply_labels(new.d, d3e2 = "think you are dishonest-31 up")
temp.d <- data.frame (new.d, d3e2)
result<-questionr::freq(temp.d$d3e2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#3
d3e3 <- as.factor(d[,"d3e3"])
# Make "*" to NA
d3e3[which(d3e3=="*")]<-"NA"
levels(d3e3) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3e3 <- ordered(d3e3, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3e3)
new.d <- apply_labels(new.d, d3e3 = "think you are dishonest-child or young")
temp.d <- data.frame (new.d, d3e3)
result<-questionr::freq(temp.d$d3e3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
D3F: Better than you
- D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
- People have acted as if they’re better than you are
- Current (from prostate cancer diagnosis to the present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Never
- 2=Rarely
- 3=Sometimes
- 4=Often
# 1
d3f1 <- as.factor(d[,"d3f1"])
# Make "*" to NA
d3f1[which(d3f1=="*")]<-"NA"
levels(d3f1) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3f1 <- ordered(d3f1, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3f1)
new.d <- apply_labels(new.d, d3f1 = "better than you-current")
temp.d <- data.frame (new.d, d3f1)
result<-questionr::freq(temp.d$d3f1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#2
d3f2 <- as.factor(d[,"d3f2"])
# Make "*" to NA
d3f2[which(d3f2=="*")]<-"NA"
levels(d3f2) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3f2 <- ordered(d3f2, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3f2)
new.d <- apply_labels(new.d, d3f2 = "better than you-31 up")
temp.d <- data.frame (new.d, d3f2)
result<-questionr::freq(temp.d$d3f2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#3
d3f3 <- as.factor(d[,"d3f3"])
# Make "*" to NA
d3f3[which(d3f3=="*")]<-"NA"
levels(d3f3) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3f3 <- ordered(d3f3, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3f3)
new.d <- apply_labels(new.d, d3f3 = "better than you-child or young")
temp.d <- data.frame (new.d, d3f3)
result<-questionr::freq(temp.d$d3f3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
D3G: Insulted
- D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
- You have been called names or insulted
- Current (from prostate cancer diagnosis to the present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Never
- 2=Rarely
- 3=Sometimes
- 4=Often
# 1
d3g1 <- as.factor(d[,"d3g1"])
# Make "*" to NA
d3g1[which(d3g1=="*")]<-"NA"
levels(d3g1) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3g1 <- ordered(d3g1, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3g1)
new.d <- apply_labels(new.d, d3g1 = "called names or insulted-current")
temp.d <- data.frame (new.d, d3g1)
result<-questionr::freq(temp.d$d3g1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#2
d3g2 <- as.factor(d[,"d3g2"])
# Make "*" to NA
d3g2[which(d3g2=="*")]<-"NA"
levels(d3g2) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3g2 <- ordered(d3g2, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3g2)
new.d <- apply_labels(new.d, d3g2 = "called names or insulted-31 up")
temp.d <- data.frame (new.d, d3g2)
result<-questionr::freq(temp.d$d3g2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#3
d3g3 <- as.factor(d[,"d3g3"])
# Make "*" to NA
d3g3[which(d3g3=="*")]<-"NA"
levels(d3g3) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3g3 <- ordered(d3g3, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3g3)
new.d <- apply_labels(new.d, d3g3 = "called names or insulted-child or young")
temp.d <- data.frame (new.d, d3g3)
result<-questionr::freq(temp.d$d3g3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
D3H: Threatened or harassed
- D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
- You have been threatened or harassed
- Current (from prostate cancer diagnosis to the present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Never
- 2=Rarely
- 3=Sometimes
- 4=Often
# 1
d3h1 <- as.factor(d[,"d3h1"])
# Make "*" to NA
d3h1[which(d3h1=="*")]<-"NA"
levels(d3h1) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3h1 <- ordered(d3h1, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3h1)
new.d <- apply_labels(new.d, d3h1 = "threatened or harassed-current")
temp.d <- data.frame (new.d, d3h1)
result<-questionr::freq(temp.d$d3h1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#2
d3h2 <- as.factor(d[,"d3h2"])
# Make "*" to NA
d3h2[which(d3e1=="*")]<-"NA"
levels(d3h2) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3h2 <- ordered(d3h2, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3h2)
new.d <- apply_labels(new.d, d3h2 = "threatened or harassed-31 up")
temp.d <- data.frame (new.d, d3h2)
result<-questionr::freq(temp.d$d3h2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#3
d3h3 <- as.factor(d[,"d3h3"])
# Make "*" to NA
d3h3[which(d3h3=="*")]<-"NA"
levels(d3h3) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3h3 <- ordered(d3h3, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3h3)
new.d <- apply_labels(new.d, d3h3 = "threatened or harassed-child or young")
temp.d <- data.frame (new.d, d3h3)
result<-questionr::freq(temp.d$d3h3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
D3I: Followed around in stores
- D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
- You have been followed around in stores
- Current (from prostate cancer diagnosis to the present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Never
- 2=Rarely
- 3=Sometimes
- 4=Often
# 1
d3i1 <- as.factor(d[,"d3i1"])
# Make "*" to NA
d3i1[which(d3e1=="*")]<-"NA"
levels(d3i1) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3i1 <- ordered(d3i1, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3i1)
new.d <- apply_labels(new.d, d3i1 = "be followed-current")
temp.d <- data.frame (new.d, d3i1)
result<-questionr::freq(temp.d$d3i1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#2
d3i2 <- as.factor(d[,"d3i2"])
# Make "*" to NA
d3i1[which(d3i1=="*")]<-"NA"
levels(d3i2) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3i2 <- ordered(d3i2, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3i2)
new.d <- apply_labels(new.d, d3i2 = "be followed-31 up")
temp.d <- data.frame (new.d, d3i2)
result<-questionr::freq(temp.d$d3i2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#3
d3i3 <- as.factor(d[,"d3i3"])
# Make "*" to NA
d3i1[which(d3i1=="*")]<-"NA"
levels(d3i3) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3i3 <- ordered(d3i3, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3i3)
new.d <- apply_labels(new.d, d3i3 = "be followed-child or young")
temp.d <- data.frame (new.d, d3i3)
result<-questionr::freq(temp.d$d3i3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
D3J: How stressful
- D3. In your day-to-day life, during the following 3 time periods, how often have any of the following things happened to you because of your race/ethnicity?
- How stressful has any of the above experience (a-i) of unfair treatment usually been for you?
- Current (from prostate cancer diagnosis to the present)
- Age 31 up to just before prostate cancer diagnosis
- Childhood or young adult life (up to age 30)
- 1=Never
- 2=Rarely
- 3=Sometimes
- 4=Often
# 1
d3j1 <- as.factor(d[,"d3j1"])
# Make "*" to NA
d3j1[which(d3j1=="*")]<-"NA"
levels(d3j1) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3j1 <- ordered(d3j1, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3j1)
new.d <- apply_labels(new.d, d3j1 = "How stressful-current")
temp.d <- data.frame (new.d, d3j1)
result<-questionr::freq(temp.d$d3j1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Current (from prostate cancer diagnosis to the present)")
1. Current (from prostate cancer diagnosis to the present)
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#2
d3j2 <- as.factor(d[,"d3j2"])
# Make "*" to NA
d3j2[which(d3j2=="*")]<-"NA"
levels(d3j2) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3j2 <- ordered(d3j2, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3j2)
new.d <- apply_labels(new.d, d3j2 = "How stressful-31 up")
temp.d <- data.frame (new.d, d3j2)
result<-questionr::freq(temp.d$d3j2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Age 31 up to just before prostate cancer diagnosis")
2. Age 31 up to just before prostate cancer diagnosis
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#3
d3j3 <- as.factor(d[,"d3j3"])
# Make "*" to NA
d3j3[which(d3j3=="*")]<-"NA"
levels(d3j3) <- list(Never="1",
Rarely="2",
Sometimes="3",
Often="4")
d3j3 <- ordered(d3j3, c("Never","Rarely","Sometimes","Often"))
new.d <- data.frame(new.d, d3j3)
new.d <- apply_labels(new.d, d3j3 = "How stressful-child or young")
temp.d <- data.frame (new.d, d3j3)
result<-questionr::freq(temp.d$d3j3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Childhood or young adult life (up to age 30)")
3. Childhood or young adult life (up to age 30)
| Never |
0 |
NaN |
NaN |
| Rarely |
0 |
NaN |
NaN |
| Sometimes |
0 |
NaN |
NaN |
| Often |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
D4: How you currently see yourself
- D4. These statements are about how you currently see yourself. Indicate your level of agreement or disagreement with each statement.
- You’ve always felt that you could make of your life pretty much what you wanted to make of it.
- Once you make up your mind to do something, you stay with it until the job is completely done.
- You like doing things that other people thought could not be done.
- When things don’t go the way you want them to, that just makes you work even harder.
- Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
- It’s not always easy, but you manage to find a way to do the things you really need to get done.
- Very seldom have you been disappointed by the results of your hard work.
- You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
- In the past, even when things got really tough, you never lost sight of your goals.
- It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
- You don’t let your personal feelings get in the way of doing a job.
- Hard work has really helped you to get ahead in life.
- 1=Strongly Agree
- 2=Somewhat Agree
- 3=Somewhat Disagree
- 4=Strongly Disagree
# a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
d4a <- as.factor(d[,"d4a"])
# Make "*" to NA
d4a[which(d4a=="*")]<-"NA"
levels(d4a) <- list(Strongly_Agree ="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d4a <- ordered(d4a, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d4a)
new.d <- apply_labels(new.d, d4a = "make life")
temp.d <- data.frame (new.d, d4a)
result<-questionr::freq(temp.d$d4a,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.")
a. You’ve always felt that you could make of your life pretty much what you wanted to make of it.
| Strongly_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Strongly_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
# b. Once you make up your mind to do something, you stay with it until the job is completely done.
d4b <- as.factor(d[,"d4b"])
# Make "*" to NA
d4b[which(d4b=="*")]<-"NA"
levels(d4b) <- list(Strongly_Agree ="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d4b <- ordered(d4b, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d4b)
new.d <- apply_labels(new.d, d4b = "until job is done")
temp.d <- data.frame (new.d, d4b)
result<-questionr::freq(temp.d$d4b,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "b. Once you make up your mind to do something, you stay with it until the job is completely done.")
b. Once you make up your mind to do something, you stay with it until the job is completely done.
| Strongly_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Strongly_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
# c. You like doing things that other people thought could not be done.
d4c <- as.factor(d[,"d4c"])
# Make "*" to NA
d4c[which(d4c=="*")]<-"NA"
levels(d4c) <- list(Strongly_Agree ="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d4c <- ordered(d4c, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d4c)
new.d <- apply_labels(new.d, d4c = "until job is done")
temp.d <- data.frame (new.d, d4c)
result<-questionr::freq(temp.d$d4c,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "c. You like doing things that other people thought could not be done.")
c. You like doing things that other people thought could not be done.
| Strongly_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Strongly_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
# d. When things don’t go the way you want them to, that just makes you work even harder.
d4d <- as.factor(d[,"d4d"])
# Make "*" to NA
d4d[which(d4d=="*")]<-"NA"
levels(d4d) <- list(Strongly_Agree ="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d4d <- ordered(d4d, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d4d)
new.d <- apply_labels(new.d, d4d = "until job is done")
temp.d <- data.frame (new.d, d4d)
result<-questionr::freq(temp.d$d4d,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "d. When things don’t go the way you want them to, that just makes you work even harder.")
d. When things don’t go the way you want them to, that just makes you work even harder.
| Strongly_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Strongly_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
# e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
d4e <- as.factor(d[,"d4e"])
# Make "*" to NA
d4e[which(d4e=="*")]<-"NA"
levels(d4e) <- list(Strongly_Agree ="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d4e <- ordered(d4e, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d4e)
new.d <- apply_labels(new.d, d4e = "do it yourself")
temp.d <- data.frame (new.d, d4e)
result<-questionr::freq(temp.d$d4e,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.")
e. Sometimes, you feel that if anything is going to be done right, you have to do it yourself.
| Strongly_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Strongly_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
# f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
d4f <- as.factor(d[,"d4f"])
# Make "*" to NA
d4f[which(d4f=="*")]<-"NA"
levels(d4f) <- list(Strongly_Agree ="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d4f <- ordered(d4f, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d4f)
new.d <- apply_labels(new.d, d4f = "not easy but get it done")
temp.d <- data.frame (new.d, d4f)
result<-questionr::freq(temp.d$d4f,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "f. It’s not always easy, but you manage to find a way to do the things you really need to get done.")
f. It’s not always easy, but you manage to find a way to do the things you really need to get done.
| Strongly_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Strongly_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
# g. Very seldom have you been disappointed by the results of your hard work.
d4g <- as.factor(d[,"d4g"])
# Make "*" to NA
d4g[which(d4g=="*")]<-"NA"
levels(d4g) <- list(Strongly_Agree ="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d4g <- ordered(d4g, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d4g)
new.d <- apply_labels(new.d, d4g = "seldom disappointed")
temp.d <- data.frame (new.d, d4g)
result<-questionr::freq(temp.d$d4g,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "g. Very seldom have you been disappointed by the results of your hard work.")
g. Very seldom have you been disappointed by the results of your hard work.
| Strongly_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Strongly_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
# h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
d4h <- as.factor(d[,"d4h"])
# Make "*" to NA
d4h[which(d4h=="*")]<-"NA"
levels(d4h) <- list(Strongly_Agree ="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d4h <- ordered(d4h, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d4h)
new.d <- apply_labels(new.d, d4h = "stand up for believes")
temp.d <- data.frame (new.d, d4h)
result<-questionr::freq(temp.d$d4h,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.")
h. You feel you are the kind of individual who stands up for what he believes in, regardless of the consequences.
| Strongly_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Strongly_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
# i. In the past, even when things got really tough, you never lost sight of your goals.
d4i <- as.factor(d[,"d4i"])
# Make "*" to NA
d4i[which(d4i=="*")]<-"NA"
levels(d4i) <- list(Strongly_Agree ="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d4i <- ordered(d4i, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d4i)
new.d <- apply_labels(new.d, d4i = "tough but never lost")
temp.d <- data.frame (new.d, d4i)
result<-questionr::freq(temp.d$d4i,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "i. In the past, even when things got really tough, you never lost sight of your goals.")
i. In the past, even when things got really tough, you never lost sight of your goals.
| Strongly_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Strongly_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
#j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
d4j <- as.factor(d[,"d4j"])
# Make "*" to NA
d4j[which(d4j=="*")]<-"NA"
levels(d4j) <- list(Strongly_Agree ="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d4j <- ordered(d4j, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d4j)
new.d <- apply_labels(new.d, d4j = "the way you want to do matters")
temp.d <- data.frame (new.d, d4j)
result<-questionr::freq(temp.d$d4j,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.")
j. It’s important for you to be able to do things the way you want to do them rather than the way other people want you to do them.
| Strongly_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Strongly_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
#k. You don’t let your personal feelings get in the way of doing a job.
d4k <- as.factor(d[,"d4k"])
# Make "*" to NA
d4k[which(d4k=="*")]<-"NA"
levels(d4k) <- list(Strongly_Agree ="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d4k <- ordered(d4k, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d4k)
new.d <- apply_labels(new.d, d4k = "personal feelings never get in the way of job")
temp.d <- data.frame (new.d, d4k)
result<-questionr::freq(temp.d$d4k,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "k. You don’t let your personal feelings get in the way of doing a job.")
k. You don’t let your personal feelings get in the way of doing a job.
| Strongly_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Strongly_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
#l. Hard work has really helped you to get ahead in life.
d4l <- as.factor(d[,"d4l"])
# Make "*" to NA
d4l[which(d4l=="*")]<-"NA"
levels(d4l) <- list(Strongly_Agree ="1",
Somewhat_Agree="2",
Somewhat_Disagree="3",
Strongly_Disagree="4")
d4l <- ordered(d4l, c("Strongly_Agree","Somewhat_Agree","Somewhat_Disagree","Strongly_Disagree"))
new.d <- data.frame(new.d, d4l)
new.d <- apply_labels(new.d, d4l = "hard work helps")
temp.d <- data.frame (new.d, d4l)
result<-questionr::freq(temp.d$d4l,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "l. Hard work has really helped you to get ahead in life.")
l. Hard work has really helped you to get ahead in life.
| Strongly_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_Agree |
0 |
NaN |
NaN |
NaN |
NaN |
| Somewhat_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Strongly_Disagree |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
D5: Childhood
- D5. The next questions are about the time period of your childhood, before the age of 18. These are standard questions asked in many surveys of life history. This information will allow us to understand how problems that may occur early in life may affect health later in life. This is a sensitive topic and some people may feel uncomfortable with these questions. Please keep in mind that you can skip any question you do not want to answer. All information is kept confidential. When you were growing up, during the first 18 years of your life…
- Did you live with anyone who was depressed, mentally ill, or suicidal?
- Did you live with anyone who was a problem drinker or alcoholic?
- Did you live with anyone who used illegal street drugs or who abused prescription medications?
- Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility?
- Were your parents separated or divorced?
- How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
- How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way? Do not include spanking.
- How often did a parent or adult in your home ever swear at you, insult you, or put you down?
- How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
- How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
- How often did anyone at least 5 years older than you or an adult, force you to have sex?
- 1=No
- 2=Yes
- 3=Parents not married
- 88=Don’t know/not sure
- 99=Prefer not to answer”
# a. Did you live with anyone who was depressed, mentally ill, or suicidal?
d5a <- as.factor(d[,"d5a"])
# Make "*" to NA
d5a[which(d5a=="*")]<-"NA"
levels(d5a) <- list(No="1",
Yes="2",
Dont_know_not_sure="88",
Prefer_not_to_answer="99")
d5a <- ordered(d5a, c("No","Yes","Dont_know_not_sure","Prefer_not_to_answer"))
new.d <- data.frame(new.d, d5a)
new.d <- apply_labels(new.d, d5a = "live with depressed")
temp.d <- data.frame (new.d, d5a)
result<-questionr::freq(temp.d$d5a,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "a. Did you live with anyone who was depressed, mentally ill, or suicidal?")
a. Did you live with anyone who was depressed, mentally ill, or suicidal?
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Dont_know_not_sure |
0 |
NaN |
NaN |
| Prefer_not_to_answer |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
# b. Did you live with anyone who was a problem drinker or alcoholic?
d5b <- as.factor(d[,"d5b"])
# Make "*" to NA
d5b[which(d5b=="*")]<-"NA"
levels(d5b) <- list(No="1",
Yes="2",
Dont_know_not_sure="88",
Prefer_not_to_answer="99")
d5b <- ordered(d5b, c( "No","Yes","Dont_know_not_sure","Prefer_not_to_answer"))
new.d <- data.frame(new.d, d5b)
new.d <- apply_labels(new.d, d5b = "live with alcoholic")
temp.d <- data.frame (new.d, d5b)
result<-questionr::freq(temp.d$d5b,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "b. Did you live with anyone who was a problem drinker or alcoholic?")
b. Did you live with anyone who was a problem drinker or alcoholic?
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Dont_know_not_sure |
0 |
NaN |
NaN |
| Prefer_not_to_answer |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
# c. Did you live with anyone who used illegal street drugs or who abused prescription medications?
d5c <- as.factor(d[,"d5c"])
# Make "*" to NA
d5c[which(d5c=="*")]<-"NA"
levels(d5c) <- list(No="1",
Yes="2",
Dont_know_not_sure="88",
Prefer_not_to_answer="99")
d5c <- ordered(d5c, c( "No","Yes","Dont_know_not_sure","Prefer_not_to_answer"))
new.d <- data.frame(new.d, d5c)
new.d <- apply_labels(new.d, d5c = "live with illegal street drugs")
temp.d <- data.frame (new.d, d5c)
result<-questionr::freq(temp.d$d5c,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "c. Did you live with anyone who used illegal street drugs or who abused prescription medications?")
c. Did you live with anyone who used illegal street drugs or who abused prescription medications?
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Dont_know_not_sure |
0 |
NaN |
NaN |
| Prefer_not_to_answer |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
# d. Did you live with anyone who served time or was sentenced to serve time in a prison, jail, or other correctional facility?
d5d <- as.factor(d[,"d5d"])
# Make "*" to NA
d5d[which(d5d=="*")]<-"NA"
levels(d5d) <- list(No="1",
Yes="2",
Dont_know_not_sure="88",
Prefer_not_to_answer="99")
d5d <- ordered(d5d, c( "No","Yes","Dont_know_not_sure","Prefer_not_to_answer"))
new.d <- data.frame(new.d, d5d)
new.d <- apply_labels(new.d, d5d = "live with people in a prison")
temp.d <- data.frame (new.d, d5d)
result<-questionr::freq(temp.d$d5d,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "d. Did you live with anyone who served time or was sentenced to serve time in a prison, etc?")
d. Did you live with anyone who served time or was sentenced to serve time in a prison, etc?
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Dont_know_not_sure |
0 |
NaN |
NaN |
| Prefer_not_to_answer |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
# e. Were your parents separated or divorced?
d5e <- as.factor(d[,"d5e"])
# Make "*" to NA
d5e[which(d5e=="*")]<-"NA"
levels(d5e) <- list(No="1",
Yes="2",
Not_married="3",
Dont_know_not_sure="88",
Prefer_not_to_answer="99")
d5e <- ordered(d5e, c( "No","Yes","Not_married","Dont_know_not_sure","Prefer_not_to_answer"))
new.d <- data.frame(new.d, d5e)
new.d <- apply_labels(new.d, d5e = "parents divorced")
temp.d <- data.frame (new.d, d5e)
result<-questionr::freq(temp.d$d5e,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "e. Were your parents separated or divorced?")
e. Were your parents separated or divorced?
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Not_married |
0 |
NaN |
NaN |
| Dont_know_not_sure |
0 |
NaN |
NaN |
| Prefer_not_to_answer |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
# f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
d5f <- as.factor(d[,"d5f"])
# Make "*" to NA
d5f[which(d5f=="*")]<-"NA"
levels(d5f) <- list(Never="1",
Once="2",
More_than_once="3",
Dont_know_not_sure="88",
Prefer_not_to_answer="99")
d5f <- ordered(d5f, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
new.d <- data.frame(new.d, d5f)
new.d <- apply_labels(new.d, d5f = "violence to each other")
temp.d <- data.frame (new.d, d5f)
result<-questionr::freq(temp.d$d5f,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?")
f. How often did your parents or adults in your home ever slap, hit, kick, punch or beat each other up?
| Never |
0 |
NaN |
NaN |
| Once |
0 |
NaN |
NaN |
| More_than_once |
0 |
NaN |
NaN |
| Dont_know_not_sure |
0 |
NaN |
NaN |
| Prefer_not_to_answer |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
# g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
d5g <- as.factor(d[,"d5g"])
# Make "*" to NA
d5g[which(d5g=="*")]<-"NA"
levels(d5g) <- list(Never="1",
Once="2",
More_than_once="3",
Dont_know_not_sure="88",
Prefer_not_to_answer="99")
d5g <- ordered(d5g, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
new.d <- data.frame(new.d, d5g)
new.d <- apply_labels(new.d, d5g = "violence to you")
temp.d <- data.frame (new.d, d5g)
result<-questionr::freq(temp.d$d5g,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?")
g. How often did a parent or adult in your home ever hit, beat, kick, or physically hurt you in any way?
| Never |
0 |
NaN |
NaN |
| Once |
0 |
NaN |
NaN |
| More_than_once |
0 |
NaN |
NaN |
| Dont_know_not_sure |
0 |
NaN |
NaN |
| Prefer_not_to_answer |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
# h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
d5h <- as.factor(d[,"d5h"])
# Make "*" to NA
d5h[which(d5h=="*")]<-"NA"
levels(d5h) <- list(Never="1",
Once="2",
More_than_once="3",
Dont_know_not_sure="88",
Prefer_not_to_answer="99")
d5h <- ordered(d5h, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
new.d <- data.frame(new.d, d5h)
new.d <- apply_labels(new.d, d5h = "swear insult")
temp.d <- data.frame (new.d, d5h)
result<-questionr::freq(temp.d$d5h,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?")
h. How often did a parent or adult in your home ever swear at you, insult you, or put you down?
| Never |
0 |
NaN |
NaN |
| Once |
0 |
NaN |
NaN |
| More_than_once |
0 |
NaN |
NaN |
| Dont_know_not_sure |
0 |
NaN |
NaN |
| Prefer_not_to_answer |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
# i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
d5i <- as.factor(d[,"d5i"])
# Make "*" to NA
d5i[which(d5i=="*")]<-"NA"
levels(d5i) <- list(Never="1",
Once="2",
More_than_once="3",
Dont_know_not_sure="88",
Prefer_not_to_answer="99")
d5i <- ordered(d5i, c("Never", "Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
new.d <- data.frame(new.d, d5i)
new.d <- apply_labels(new.d, d5i = "touch you sexually")
temp.d <- data.frame (new.d, d5i)
result<-questionr::freq(temp.d$d5i,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?")
i. How often did anyone at least 5 years older than you or an adult, ever touch you sexually?
| Never |
0 |
NaN |
NaN |
| Once |
0 |
NaN |
NaN |
| More_than_once |
0 |
NaN |
NaN |
| Dont_know_not_sure |
0 |
NaN |
NaN |
| Prefer_not_to_answer |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
# j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
d5j <- as.factor(d[,"d5j"])
# Make "*" to NA
d5j[which(d5j=="*")]<-"NA"
levels(d5j) <- list(Never="1",
Once="2",
More_than_once="3",
Dont_know_not_sure="88",
Prefer_not_to_answer="99")
d5j <- ordered(d5j, c("Never","Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
new.d <- data.frame(new.d, d5j)
new.d <- apply_labels(new.d, d5j = "touch them sexually")
temp.d <- data.frame (new.d, d5j)
result<-questionr::freq(temp.d$d5j,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?")
j. How often did anyone at least 5 years older than you or an adult, try to make you touch them sexually?
| Never |
0 |
NaN |
NaN |
| Once |
0 |
NaN |
NaN |
| More_than_once |
0 |
NaN |
NaN |
| Dont_know_not_sure |
0 |
NaN |
NaN |
| Prefer_not_to_answer |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
# k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
d5k <- as.factor(d[,"d5k"])
# Make "*" to NA
d5k[which(d5k=="*")]<-"NA"
levels(d5k) <- list(Never="1",
Once="2",
More_than_once="3",
Dont_know_not_sure="88",
Prefer_not_to_answer="99")
d5k <- ordered(d5k, c("Never","Once","More_than_once","Dont_know_not_sure","Prefer_not_to_answer"))
new.d <- data.frame(new.d, d5k)
new.d <- apply_labels(new.d, d5k = "forced to have sex")
temp.d <- data.frame (new.d, d5k)
result<-questionr::freq(temp.d$d5k,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "k. How often did anyone at least 5 years older than you or an adult, force you to have sex?")
k. How often did anyone at least 5 years older than you or an adult, force you to have sex?
| Never |
0 |
NaN |
NaN |
| Once |
0 |
NaN |
NaN |
| More_than_once |
0 |
NaN |
NaN |
| Dont_know_not_sure |
0 |
NaN |
NaN |
| Prefer_not_to_answer |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
E1: First indications
- E1. What were the first indications that suggested that you might have prostate cancer (before you had a prostate biopsy)? Mark all that apply.
- E1_1: 1=I had a high PSA (‘prostate specific antigen’) test
- E1_2: 1=My doctor did a digital rectal exam that indicated an abnormality
- E1_3: 1=I had urinary, sexual, or bowel problems that I went to see my doctor about
- E1_4: 1=I had bone pain that I went to see my doctor about
- E1_5: 1=I was fearful I had cancer
- E1_6: 1=Other
# 1
e1_1 <- as.factor(d[,"e1_1"])
levels(e1_1) <- list(High_PSA_test="1")
new.d <- data.frame(new.d, e1_1)
new.d <- apply_labels(new.d, e1_1 = "High_PSA_test")
temp.d <- data.frame (new.d, e1_1)
result<-questionr::freq(temp.d$e1_1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. I had a high PSA (‘prostate specific antigen’) test")
1. I had a high PSA (‘prostate specific antigen’) test
| High_PSA_test |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#2
e1_2 <- as.factor(d[,"e1_2"])
levels(e1_2) <- list(Digital_rectal_exam="1")
new.d <- data.frame(new.d, e1_2)
new.d <- apply_labels(new.d, e1_2 = "digital rectal exam")
temp.d <- data.frame (new.d, e1_2)
result<-questionr::freq(temp.d$e1_2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. My doctor did a digital rectal exam that indicated an abnormality")
2. My doctor did a digital rectal exam that indicated an abnormality
| Digital_rectal_exam |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#3
e1_3 <- as.factor(d[,"e1_3"])
e1_3[which(e1_3=="*")]<-"NA"
levels(e1_3) <- list(Digital_rectal_exam="1")
new.d <- data.frame(new.d, e1_3)
new.d <- apply_labels(new.d, e1_3 = "urinary sexual or bowel problems")
temp.d <- data.frame (new.d, e1_3)
result<-questionr::freq(temp.d$e1_3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. I had urinary, sexual, or bowel problems that I went to see my doctor about")
3. I had urinary, sexual, or bowel problems that I went to see my doctor about
| Digital_rectal_exam |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#4
e1_4 <- as.factor(d[,"e1_4"])
e1_4[which(e1_4=="*")]<-"NA"
levels(e1_4) <- list(Digital_rectal_exam="1")
new.d <- data.frame(new.d, e1_4)
new.d <- apply_labels(new.d, e1_4 = "bone pain")
temp.d <- data.frame (new.d, e1_4)
result<-questionr::freq(temp.d$e1_4,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "4. I had bone pain that I went to see my doctor about")
4. I had bone pain that I went to see my doctor about
| Digital_rectal_exam |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#5
e1_5 <- as.factor(d[,"e1_5"])
e1_5[which(e1_5=="*")]<-"NA"
levels(e1_5) <- list(Digital_rectal_exam="1")
new.d <- data.frame(new.d, e1_5)
new.d <- apply_labels(new.d, e1_5 = "fearful")
temp.d <- data.frame (new.d, e1_5)
result<-questionr::freq(temp.d$e1_5,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "5. I was fearful I had cancer")
5. I was fearful I had cancer
| Digital_rectal_exam |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
E1 Other: First indications
e1other <- d[,"e1other"]
e1other[which(e1other=="#NAME?")]<-"NA"
new.d <- data.frame(new.d, e1other)
new.d <- apply_labels(new.d, e1other = "e1other")
temp.d <- data.frame (new.d, e1other)
result<-questionr::freq(temp.d$e1other, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "E1 Other")
E2: Before diagnosis
- E2. Before you were diagnosed with prostate cancer:
- Did you have any previous prostate biopsies that were negative?
- If yes, How many?
- Did you have any previous PSA blood tests that were considered normal?
- If yes, How many?
- 1=1
- 2=2
- 3=3
- 4=4
- 5=5 or more
# 1
e2aa <- as.factor(d[,"e2aa"])
# Make "*" to NA
e2aa[which(e2aa=="*")]<-"NA"
levels(e2aa) <- list(Yes="2",
No="1",
Dont_know="88")
e2aa <- ordered(e2aa, c("Yes","No","Dont_know"))
new.d <- data.frame(new.d, e2aa)
new.d <- apply_labels(new.d, e2aa = "prostate biopsies")
temp.d <- data.frame (new.d, e2aa)
result<-questionr::freq(temp.d$e2aa,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "a. Did you have any previous prostate biopsies that were negative?")
a. Did you have any previous prostate biopsies that were negative?
| Yes |
0 |
NaN |
NaN |
| No |
0 |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#2
e2ab <- as.factor(d[,"e2ab"])
# Make "*" to NA
e2ab[which(e2ab=="*")]<-"NA"
levels(e2ab) <- list(One="1",
Two="2",
Three_more="3")
e2ab <- ordered(e2ab, c("One","Two","Three_more"))
new.d <- data.frame(new.d, e2ab)
new.d <- apply_labels(new.d, e2ab = "prostate biopsies_How many")
temp.d <- data.frame (new.d, e2ab)
result<-questionr::freq(temp.d$e2ab,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "If yes, How many?")
If yes, How many?
| One |
0 |
NaN |
NaN |
| Two |
0 |
NaN |
NaN |
| Three_more |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#3
e2ba <- as.factor(d[,"e2ba"])
# Make "*" to NA
e2ba[which(e2ba=="*")]<-"NA"
levels(e2ba) <- list(Yes="2",
No="1",
Dont_know="88")
e2ba <- ordered(e2ba, c("Yes","No","Dont_know"))
new.d <- data.frame(new.d, e2ba)
new.d <- apply_labels(new.d, e2ba = "PSA blood tests")
temp.d <- data.frame (new.d, e2ba)
result<-questionr::freq(temp.d$e2ba,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "b. Did you have any previous PSA blood tests that were considered normal?")
b. Did you have any previous PSA blood tests that were considered normal?
| Yes |
0 |
NaN |
NaN |
| No |
0 |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
#4
e2bb <- as.factor(d[,"e2bb"])
# Make "*" to NA
e2bb[which(e2bb=="*")]<-"NA"
levels(e2bb) <- list(One="1",
Two="2",
Three="3",
Four="4",
Five_more="5")
e2bb <- ordered(e2bb, c("One","Two","Threem","Four","Five_more"))
new.d <- data.frame(new.d, e2bb)
new.d <- apply_labels(new.d, e2bb = "PSA blood tests_how many")
temp.d <- data.frame (new.d, e2bb)
result<-questionr::freq(temp.d$e2bb,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "If yes, How many?")
If yes, How many?
| One |
0 |
NaN |
NaN |
| Two |
0 |
NaN |
NaN |
| Threem |
0 |
NaN |
NaN |
| Four |
0 |
NaN |
NaN |
| Five_more |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
E3: Decision about PSA blood test
- E3. Which of the following best describes your decision to have the PSA blood test that indicated that you had prostate cancer?
- 1=I made the decision alone
- 2=I made the decision together with a family member or friend
- 3=I made the decision together with a family member or friend and my doctor, nurse, or health care provider
- 4= I made the decision together with my doctor, nurse, or health care provider
- 5=My doctor, nurse, or health care provider made the decision
- 88=I do not know or remember how the decision was made
e3 <- as.factor(d[,"e3"])
# Make "*" to NA
e3[which(e3=="*")]<-"NA"
levels(e3) <- list(Alone="1",
With_family_or_friends="2",
With_family_and_doctor="3",
With_doctor="4",
Doctor_made="5",
Dont_know_or_remember="88")
e3 <- ordered(e3, c("Alone","With_family_or_friends","With_family_and_doctor","With_doctor","Doctor_made","Dont_know_or_remember"))
new.d <- data.frame(new.d, e3)
new.d <- apply_labels(new.d, e3 = "decision to have the PSA blood test")
temp.d <- data.frame (new.d, e3)
result<-questionr::freq(temp.d$e3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "E3")
E3
| Alone |
0 |
NaN |
NaN |
| With_family_or_friends |
0 |
NaN |
NaN |
| With_family_and_doctor |
0 |
NaN |
NaN |
| With_doctor |
0 |
NaN |
NaN |
| Doctor_made |
0 |
NaN |
NaN |
| Dont_know_or_remember |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
E4: Understanding of aggressiveness
- E4. When you were diagnosed with prostate cancer, what was your understanding of how aggressive your cancer might be (i.e., how likely it was that your cancer might progress).
- 1=Low risk of progression
- 2=Intermediate risk of progression
- 3=High risk of progression
- 4=Unknown risk of progression
- 88=Don’t know/Don’t remember
e4 <- as.factor(d[,"e4"])
# Make "*" to NA
e4[which(e4=="*")]<-"NA"
levels(e4) <- list(Low_risk="1",
Intermediate_risk="2",
High_risk="3",
Unknown_risk="4",
Dont_know_or_remember="88")
e4 <- ordered(e4, c("Low_risk","Intermediate_risk","High_risk","Unknown_risk","Dont_know_or_remember"))
new.d <- data.frame(new.d, e4)
new.d <- apply_labels(new.d, e4 = "how aggressive")
temp.d <- data.frame (new.d, e4)
result<-questionr::freq(temp.d$e4,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "e4")
e4
| Low_risk |
0 |
NaN |
NaN |
| Intermediate_risk |
0 |
NaN |
NaN |
| High_risk |
0 |
NaN |
NaN |
| Unknown_risk |
0 |
NaN |
NaN |
| Dont_know_or_remember |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
E5: Gleason score
- E5. What was your Gleason score when you were diagnosed with prostate cancer?
- 1=6 or less
- 2=7
- 3=8-10
- 88=Don’t know
e5 <- as.factor(d[,"e5"])
# Make "*" to NA
e5[which(e5=="*")]<-"NA"
levels(e5) <- list(Six_less="1",
Seven="2",
Eight_to_ten="3",
Dont_know="88")
e5 <- ordered(e5, c("Six_less","Seven","Eight_to_ten","Dont_know"))
new.d <- data.frame(new.d, e5)
new.d <- apply_labels(new.d, e5 = "Gleason score")
temp.d <- data.frame (new.d, e5)
result<-questionr::freq(temp.d$e5,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "e5")
e5
| Six_less |
0 |
NaN |
NaN |
| Seven |
0 |
NaN |
NaN |
| Eight_to_ten |
0 |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
E6: Understanding of stage
- E6. What was your understanding of the stage of your prostate cancer when you were diagnosed?
- 1=Localized, confined to prostate
- 2=Regional, tumor extended to regions around the prostate
- 3=Distant, tumor extended to bones or other parts of body
- 88=Don’t know about the stage
e6 <- as.factor(d[,"e6"])
# Make "*" to NA
e6[which(e6=="*")]<-"NA"
levels(e6) <- list(Localized="1",
Regional="2",
Distant="3",
Dont_know="88")
e6 <- ordered(e6, c("Localized","Regional","Distant","Dont_know"))
new.d <- data.frame(new.d, e6)
new.d <- apply_labels(new.d, e6 = "Stage")
temp.d <- data.frame (new.d, e6)
result<-questionr::freq(temp.d$e6,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "e6")
e6
| Localized |
0 |
NaN |
NaN |
| Regional |
0 |
NaN |
NaN |
| Distant |
0 |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
E7: MRI guided biopsy
- E7. Did you have a Magnetic Resonance Imaging (MRI)-guided biopsy to diagnose your cancer? (This is a different type of biopsy than the standard ultrasound biopsy that involves taking 12 random biopsy core samples. Instead, you would be placed in a large donut shaped machine that can be noisy. With assistance from the MRI, 2-3 targeted biopsies would be taken in areas of the tumor shown to be most aggressive.)
e7 <- as.factor(d[,"e7"])
# Make "*" to NA
e7[which(e7=="*")]<-"NA"
levels(e7) <- list(No="1",
Yes="2",
Dont_know="88")
e7 <- ordered(e7, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, e7)
new.d <- apply_labels(new.d, e7 = "Stage")
temp.d <- data.frame (new.d, e7)
result<-questionr::freq(temp.d$e7,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "e7")
e7
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
E8: Decision about treatment
- E8. How did you make your treatment decision?
- 1=I made the decision alone
- 2=I made the decision together with a family member or friend
- 3=I made the decision together with a family member or friend and my doctor, nurse, or health care provider
- 4=I made the decision together with my doctor, nurse, or health care provider
- 5=My doctor , nurse, or health care provider made the decision
- 6=I don’t know or remember how the decision was made
e8 <- as.factor(d[,"e8"])
# Make "*" to NA
e8[which(e8=="*")]<-"NA"
levels(e8) <- list(Alone="1",
With_family_or_friends="2",
With_family_and_doctor="3",
With_doctor="4",
Doctor_made="5",
Dont_know_or_remember="88")
e8 <- ordered(e8, c("Alone","With_family_or_friends","With_family_and_doctor","With_doctor","Doctor_made","Dont_know_or_remember"))
new.d <- data.frame(new.d, e8)
new.d <- apply_labels(new.d, e8 = "treatment decision")
temp.d <- data.frame (new.d, e8)
result<-questionr::freq(temp.d$e8,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "e8")
e8
| Alone |
0 |
NaN |
NaN |
| With_family_or_friends |
0 |
NaN |
NaN |
| With_family_and_doctor |
0 |
NaN |
NaN |
| With_doctor |
0 |
NaN |
NaN |
| Doctor_made |
0 |
NaN |
NaN |
| Dont_know_or_remember |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
E9: The most important factors of tx
- E9. What were the most important factors you considered in making your treatment decision? Mark all that apply.
- E9_1: 1=Best chance for cure of my cancer
- E9_2: 1=Minimize side effects related to sexual function
- E9_3: 1=Minimize side effects related to urinary function
- E9_4: 1=Minimize side effects related to bowel function
- E9_5: 1=Minimize financial cost
- E9_6: 1=Amount of time and travel required to receive treatments
- E9_7: 1=Length of recovery time
- E9_8: 1=Amount of time away from work
- E9_9: 1=Burden on family members
- E9_10: 1=Reduce worry and concern about cancer
e9_1 <- as.factor(d[,"e9_1"])
levels(e9_1) <- list(Best_for_cure="1")
new.d <- data.frame(new.d, e9_1)
new.d <- apply_labels(new.d, e9_1 = "Best for cure")
temp.d <- data.frame (new.d, e9_1)
result<-questionr::freq(temp.d$e9_1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Best chance for cure of my cancer")
1. Best chance for cure of my cancer
| Best_for_cure |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
e9_2 <- as.factor(d[,"e9_2"])
levels(e9_2) <- list(side_effects_sexual="1")
new.d <- data.frame(new.d, e9_2)
new.d <- apply_labels(new.d, e9_2 = "side effects sexual")
temp.d <- data.frame (new.d, e9_2)
result<-questionr::freq(temp.d$e9_2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Minimize side effects related to sexual function")
2. Minimize side effects related to sexual function
| side_effects_sexual |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
e9_3 <- as.factor(d[,"e9_3"])
levels(e9_3) <- list(side_effects_urinary="1")
new.d <- data.frame(new.d, e9_3)
new.d <- apply_labels(new.d, e9_3 = "side effects urinary")
temp.d <- data.frame (new.d, e9_3)
result<-questionr::freq(temp.d$e9_3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Minimize side effects related to urinary function")
3. Minimize side effects related to urinary function
| side_effects_urinary |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
e9_4 <- as.factor(d[,"e9_4"])
levels(e9_4) <- list(side_effects_bowel="1")
new.d <- data.frame(new.d, e9_4)
new.d <- apply_labels(new.d, e9_4 = "side effects bowel")
temp.d <- data.frame (new.d, e9_4)
result<-questionr::freq(temp.d$e9_4,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "4. Minimize side effects related to bowel function")
4. Minimize side effects related to bowel function
| side_effects_bowel |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
e9_5 <- as.factor(d[,"e9_5"])
levels(e9_5) <- list(financial_cost="1")
new.d <- data.frame(new.d, e9_5)
new.d <- apply_labels(new.d, e9_5 = "financial cost")
temp.d <- data.frame (new.d, e9_5)
result<-questionr::freq(temp.d$e9_5,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "5. Minimize financial cost")
5. Minimize financial cost
| financial_cost |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
e9_6 <- as.factor(d[,"e9_6"])
levels(e9_6) <- list(time_and_travel="1")
new.d <- data.frame(new.d, e9_6)
new.d <- apply_labels(new.d, e9_6 = "time and travel")
temp.d <- data.frame (new.d, e9_6)
result<-questionr::freq(temp.d$e9_6,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "6. Amount of time and travel required to receive treatments")
6. Amount of time and travel required to receive treatments
| time_and_travel |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
e9_7 <- as.factor(d[,"e9_7"])
levels(e9_7) <- list(recovery_time="1")
new.d <- data.frame(new.d, e9_7)
new.d <- apply_labels(new.d, e9_7 = "recovery time")
temp.d <- data.frame (new.d, e9_7)
result<-questionr::freq(temp.d$e9_7,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "7. Length of recovery time")
7. Length of recovery time
| recovery_time |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
e9_8 <- as.factor(d[,"e9_8"])
levels(e9_8) <- list(time_away_from_work="1")
new.d <- data.frame(new.d, e9_8)
new.d <- apply_labels(new.d, e9_8 = "time away from work")
temp.d <- data.frame (new.d, e9_8)
result<-questionr::freq(temp.d$e9_8,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "8. Amount of time away from work")
8. Amount of time away from work
| time_away_from_work |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
e9_9 <- as.factor(d[,"e9_9"])
levels(e9_9) <- list(family_burden="1")
new.d <- data.frame(new.d, e9_9)
new.d <- apply_labels(new.d, e9_9 = "family burden")
temp.d <- data.frame (new.d, e9_9)
result<-questionr::freq(temp.d$e9_9,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "9. Burden on family members")
9. Burden on family members
| family_burden |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
e9_10 <- as.factor(d[,"e9_10"])
levels(e9_10) <- list(Reduce_worry_concern="1")
new.d <- data.frame(new.d, e9_10)
new.d <- apply_labels(new.d, e9_10 = "Reduce worry and concern")
temp.d <- data.frame (new.d, e9_10)
result<-questionr::freq(temp.d$e9_10,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "10. Reduce worry and concern about cancer")
10. Reduce worry and concern about cancer
| Reduce_worry_concern |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
E10: Recieved treatment
- E10. Please mark all the treatments that you have received for your prostate cancer? Mark all that apply.
- E10_1: 1=Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
- E10_2: 1=Active Surveillance or watchful waiting
- E10_3: 1=Prostate surgery (prostatectomy)
- E10_4: 1=Radiation to the prostate
- E10_5: 1=Hormonal treatments
- E10_6: 1=Provenge/immunotherapy (Sipuleucel T)
- E10_7: 1=Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
- E10_8: 1=Other treatments to the prostate (HIFU (High Intensity Focused Ultrasound), RFA (Radio Frequency Ablation), laser, focal therapy, cryotherapy (freezing of the prostate))
e10_1 <- as.factor(d[,"e10_1"])
levels(e10_1) <- list(no_treatment="1")
new.d <- data.frame(new.d, e10_1)
new.d <- apply_labels(new.d, e10_1 = "no treatment")
temp.d <- data.frame (new.d, e10_1)
result<-questionr::freq(temp.d$e10_1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).")
1. Haven’t had any treatment yet (and not specifically on active surveillance or watchful waiting).
| no_treatment |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
e10_2 <- as.factor(d[,"e10_2"])
levels(e10_2) <- list(Active_Surveillance="1")
new.d <- data.frame(new.d, e10_2)
new.d <- apply_labels(new.d, e10_2 = "Active Surveillance")
temp.d <- data.frame (new.d, e10_2)
result<-questionr::freq(temp.d$e10_2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Active Surveillance or watchful waiting")
2. Active Surveillance or watchful waiting
| Active_Surveillance |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
e10_3 <- as.factor(d[,"e10_3"])
levels(e10_3) <- list(prostatectomy="1")
new.d <- data.frame(new.d, e10_3)
new.d <- apply_labels(new.d, e10_3 = "prostatectomy")
temp.d <- data.frame (new.d, e10_3)
result<-questionr::freq(temp.d$e10_3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Prostate surgery (prostatectomy)")
3. Prostate surgery (prostatectomy)
| prostatectomy |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
e10_4 <- as.factor(d[,"e10_4"])
levels(e10_4) <- list(Radiation="1")
new.d <- data.frame(new.d, e10_4)
new.d <- apply_labels(new.d, e10_4 = "Radiation")
temp.d <- data.frame (new.d, e10_4)
result<-questionr::freq(temp.d$e10_4,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "4. Radiation to the prostate")
4. Radiation to the prostate
| Radiation |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
e10_5 <- as.factor(d[,"e10_5"])
levels(e10_5) <- list(Hormonal_treatments="1")
new.d <- data.frame(new.d, e10_5)
new.d <- apply_labels(new.d, e10_5 = "Hormonal treatments")
temp.d <- data.frame (new.d, e10_5)
result<-questionr::freq(temp.d$e10_5,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "5. Hormonal treatments")
5. Hormonal treatments
| Hormonal_treatments |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
e10_6 <- as.factor(d[,"e10_6"])
levels(e10_6) <- list(Provenge_immunotherapy="1")
new.d <- data.frame(new.d, e10_6)
new.d <- apply_labels(new.d, e10_6 = "Provenge immunotherapy")
temp.d <- data.frame (new.d, e10_6)
result<-questionr::freq(temp.d$e10_6,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "6. Provenge/immunotherapy (Sipuleucel T)")
6. Provenge/immunotherapy (Sipuleucel T)
| Provenge_immunotherapy |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
e10_7 <- as.factor(d[,"e10_7"])
levels(e10_7) <- list(Chemotherapy="1")
new.d <- data.frame(new.d, e10_7)
new.d <- apply_labels(new.d, e10_7 = "Chemotherapy")
temp.d <- data.frame (new.d, e10_7)
result<-questionr::freq(temp.d$e10_7,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)")
7. Chemotherapy (docetaxel, cabazitaxel, other chemotherapy)
| Chemotherapy |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
e10_8 <- as.factor(d[,"e10_8"])
levels(e10_8) <- list(Other="1")
new.d <- data.frame(new.d, e10_8)
new.d <- apply_labels(new.d, e10_8 = "Other")
temp.d <- data.frame (new.d, e10_8)
result<-questionr::freq(temp.d$e10_8,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "8. Other treatments to the prostate ")
8. Other treatments to the prostate
| Other |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
E10-3 Prostatectomy
- E10_3. Prostate surgery (prostatectomy), indicate which type(s):
- E10_3_1: 1=Robotic or laproscopic surgery resulting in removal of the prostate
- E10_3_2: 1=Open surgical removal of the prostate (using a long incision)
- E10_3_3: 1=Had surgery but unsure of type
e10_3_1 <- as.factor(d[,"e10_3_1"])
levels(e10_3_1) <- list(Robotic_laproscopic_surgery="1")
new.d <- data.frame(new.d, e10_3_1)
new.d <- apply_labels(new.d, e10_3_1 = "Robotic or laproscopic surgery")
temp.d <- data.frame (new.d, e10_3_1)
result<-questionr::freq(temp.d$e10_3_1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Robotic or laproscopic surgery resulting in removal of the prostate")
1. Robotic or laproscopic surgery resulting in removal of the prostate
| Robotic_laproscopic_surgery |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
e10_3_2 <- as.factor(d[,"e10_3_2"])
levels(e10_3_2) <- list(Open_surgical_removal="1")
new.d <- data.frame(new.d, e10_3_2)
new.d <- apply_labels(new.d, e10_3_2 = "Open surgical removal")
temp.d <- data.frame (new.d, e10_3_2)
result<-questionr::freq(temp.d$e10_3_2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. Open surgical removal of the prostate (using a long incision)")
2. Open surgical removal of the prostate (using a long incision)
| Open_surgical_removal |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
e10_3_3 <- as.factor(d[,"e10_3_3"])
levels(e10_3_3) <- list(unsure_of_type="1")
new.d <- data.frame(new.d, e10_3_3)
new.d <- apply_labels(new.d, e10_3_3 = "unsure of type")
temp.d <- data.frame (new.d, e10_3_3)
result<-questionr::freq(temp.d$e10_3_3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Had surgery but unsure of type")
3. Had surgery but unsure of type
| unsure_of_type |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
E10-4 Radiation
- E10_4. Radiation to the prostate, indicate which type(s):
- E10_4_1: 1=External beam radiation, where beams are aimed from the outside of your body (including IMRT (Intensity Modulated Radiation Therapy), IGRT (Image-Guided Radiation Therapy), arc therapy, proton beam, cyberknife, or 3D-conformal beam therapy)
- E10_4_2: 1 = Insertion of radiation seed/roods (brachytherapy)
- E10_4_3: 1=Other types of radiation therapy, or unsure of what type
e10_4_1 <- as.factor(d[,"e10_4_1"])
levels(e10_4_1) <- list(External_beam_radiation="1")
new.d <- data.frame(new.d, e10_4_1)
new.d <- apply_labels(new.d, e10_4_1 = "External beam radiation")
temp.d <- data.frame (new.d, e10_4_1)
result<-questionr::freq(temp.d$e10_4_1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. External beam radiation")
1. External beam radiation
| External_beam_radiation |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
e10_4_2 <- as.factor(d[,"e10_4_2"])
levels(e10_4_2) <- list(brachytherapy="1")
new.d <- data.frame(new.d, e10_4_2)
new.d <- apply_labels(new.d, e10_4_2 = "brachytherapy")
temp.d <- data.frame (new.d, e10_4_2)
result<-questionr::freq(temp.d$e10_4_2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. brachytherapy")
2. brachytherapy
| brachytherapy |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
e10_4_3 <- as.factor(d[,"e10_4_3"])
levels(e10_4_3) <- list(Other_types="1")
new.d <- data.frame(new.d, e10_4_3)
new.d <- apply_labels(new.d, e10_4_3 = "Other types")
temp.d <- data.frame (new.d, e10_4_3)
result<-questionr::freq(temp.d$e10_4_3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Other types")
3. Other types
| Other_types |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
E10-5 Hormonal treatments
- E10_5. Hormonal treatments, indicate which type(s):
- E10_5_1: 1=Hormone shots (Lupron, Zoladex, Firmagon, Eligard, Vantas)
- E10_5_2: 1= Surgical removal of testicles (orchiectomy)
- E10_5_3: 1=Casodex (bicalutamide) or Eulexin (flutamide) pills
- E10_5_4: 1=Zytiga (abiraterone) or Xtandi (enzalutamide) pills
- E10_5_5: 1=Had hormone treatment, but unsure of type
e10_5_1 <- as.factor(d[,"e10_5_1"])
levels(e10_5_1) <- list(Hormone_shots="1")
new.d <- data.frame(new.d, e10_5_1)
new.d <- apply_labels(new.d, e10_5_1 = "Hormone shots")
temp.d <- data.frame (new.d, e10_5_1)
result<-questionr::freq(temp.d$e10_5_1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "1. Hormone shots")
1. Hormone shots
| Hormone_shots |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
e10_5_2 <- as.factor(d[,"e10_5_2"])
levels(e10_5_2) <- list(orchiectomy="1")
new.d <- data.frame(new.d, e10_5_2)
new.d <- apply_labels(new.d, e10_5_2 = "orchiectomy")
temp.d <- data.frame (new.d, e10_5_2)
result<-questionr::freq(temp.d$e10_5_2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "2. orchiectomy")
2. orchiectomy
| orchiectomy |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
e10_5_3 <- as.factor(d[,"e10_5_3"])
levels(e10_5_3) <- list(Casodex_Eulexin="1")
new.d <- data.frame(new.d, e10_5_3)
new.d <- apply_labels(new.d, e10_5_3 = "Casodex or Eulexin pills")
temp.d <- data.frame (new.d, e10_5_3)
result<-questionr::freq(temp.d$e10_5_3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "3. Casodex or Eulexin pills")
3. Casodex or Eulexin pills
| Casodex_Eulexin |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
e10_5_4 <- as.factor(d[,"e10_5_4"])
levels(e10_5_4) <- list(Zytiga_Xtandi="1")
new.d <- data.frame(new.d, e10_5_4)
new.d <- apply_labels(new.d, e10_5_4 = "Zytiga or Xtandi pills")
temp.d <- data.frame (new.d, e10_5_4)
result<-questionr::freq(temp.d$e10_5_4,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "4. Zytiga or Xtandi pills")
4. Zytiga or Xtandi pills
| Zytiga_Xtandi |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
e10_5_5 <- as.factor(d[,"e10_5_5"])
levels(e10_5_5) <- list(unsure_type="1")
new.d <- data.frame(new.d, e10_5_5)
new.d <- apply_labels(new.d, e10_5_5 = "unsure of type")
temp.d <- data.frame (new.d, e10_5_5)
result<-questionr::freq(temp.d$e10_5_5,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "5. unsure of type")
5. unsure of type
| unsure_type |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
E11: Treatment decision
- E11. Your treatment decision: How true is each of the following statements for you?
- I had all the information I needed when a treatment was chosen for my prostate cancer
- My doctors told me the whole story about the effects of treatment
- I knew the right questions to ask my doctor
- I had enough time to make a decision about my treatment
- I am satisfied with the choices I made in treating my prostate cancer
- I would recommend the treatment I had to a close relative or friend
- 1=Not at all
- 2=A little bit
- 3=Somewhat
- 4=Quite a bit
- 5=Very much
e11a <- as.factor(d[,"e11a"])
# Make "*" to NA
e11a[which(e11a=="*")]<-"NA"
levels(e11a) <- list(Not_at_all="1",
A_little_bit="2",
Somewhat="3",
Quite_a_bit="4",
Very_much="5")
new.d <- data.frame(new.d, e11a)
new.d <- apply_labels(new.d, e11a = "all info")
temp.d <- data.frame (new.d, e11a)
result<-questionr::freq(temp.d$e11a,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "a. I had all the information I needed when a treatment was chosen for my prostate cancer")
a. I had all the information I needed when a treatment was chosen for my prostate cancer
| Not_at_all |
0 |
NaN |
NaN |
| A_little_bit |
0 |
NaN |
NaN |
| Somewhat |
0 |
NaN |
NaN |
| Quite_a_bit |
0 |
NaN |
NaN |
| Very_much |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
e11b <- as.factor(d[,"e11b"])
# Make "*" to NA
e11b[which(e11b=="*")]<-"NA"
levels(e11b) <- list(Not_at_all="1",
A_little_bit="2",
Somewhat="3",
Quite_a_bit="4",
Very_much="5")
new.d <- data.frame(new.d, e11b)
new.d <- apply_labels(new.d, e11b = "be told about effects")
temp.d <- data.frame (new.d, e11b)
result<-questionr::freq(temp.d$e11b,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "b. My doctors told me the whole story about the effects of treatment")
b. My doctors told me the whole story about the effects of treatment
| Not_at_all |
0 |
NaN |
NaN |
| A_little_bit |
0 |
NaN |
NaN |
| Somewhat |
0 |
NaN |
NaN |
| Quite_a_bit |
0 |
NaN |
NaN |
| Very_much |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
e11c <- as.factor(d[,"e11c"])
# Make "*" to NA
e11c[which(e11c=="*")]<-"NA"
levels(e11c) <- list(Not_at_all="1",
A_little_bit="2",
Somewhat="3",
Quite_a_bit="4",
Very_much="5")
new.d <- data.frame(new.d, e11c)
new.d <- apply_labels(new.d, e11c = "right questions to ask")
temp.d <- data.frame (new.d, e11c)
result<-questionr::freq(temp.d$e11c,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "c. I knew the right questions to ask my doctor")
c. I knew the right questions to ask my doctor
| Not_at_all |
0 |
NaN |
NaN |
| A_little_bit |
0 |
NaN |
NaN |
| Somewhat |
0 |
NaN |
NaN |
| Quite_a_bit |
0 |
NaN |
NaN |
| Very_much |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
e11d <- as.factor(d[,"e11d"])
# Make "*" to NA
e11d[which(e11d=="*")]<-"NA"
levels(e11d) <- list(Not_at_all="1",
A_little_bit="2",
Somewhat="3",
Quite_a_bit="4",
Very_much="5")
new.d <- data.frame(new.d, e11d)
new.d <- apply_labels(new.d, e11d = "enough time to decide")
temp.d <- data.frame (new.d, e11d)
result<-questionr::freq(temp.d$e11d,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "d. I had enough time to make a decision about my treatment")
d. I had enough time to make a decision about my treatment
| Not_at_all |
0 |
NaN |
NaN |
| A_little_bit |
0 |
NaN |
NaN |
| Somewhat |
0 |
NaN |
NaN |
| Quite_a_bit |
0 |
NaN |
NaN |
| Very_much |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
e11e <- as.factor(d[,"e11e"])
# Make "*" to NA
e11e[which(e11e=="*")]<-"NA"
levels(e11e) <- list(Not_at_all="1",
A_little_bit="2",
Somewhat="3",
Quite_a_bit="4",
Very_much="5")
new.d <- data.frame(new.d, e11e)
new.d <- apply_labels(new.d, e11e = "satisfied with the choices")
temp.d <- data.frame (new.d, e11e)
result<-questionr::freq(temp.d$e11e,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "e. I am satisfied with the choices I made in treating my prostate cancer")
e. I am satisfied with the choices I made in treating my prostate cancer
| Not_at_all |
0 |
NaN |
NaN |
| A_little_bit |
0 |
NaN |
NaN |
| Somewhat |
0 |
NaN |
NaN |
| Quite_a_bit |
0 |
NaN |
NaN |
| Very_much |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
e11f <- as.factor(d[,"e11f"])
# Make "*" to NA
e11f[which(e11f=="*")]<-"NA"
levels(e11f) <- list(Not_at_all="1",
A_little_bit="2",
Somewhat="3",
Quite_a_bit="4",
Very_much="5")
new.d <- data.frame(new.d, e11f)
new.d <- apply_labels(new.d, e11f = "would recommend")
temp.d <- data.frame (new.d, e11f)
result<-questionr::freq(temp.d$e11f,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "f. I would recommend the treatment I had to a close relative or friend")
f. I would recommend the treatment I had to a close relative or friend
| Not_at_all |
0 |
NaN |
NaN |
| A_little_bit |
0 |
NaN |
NaN |
| Somewhat |
0 |
NaN |
NaN |
| Quite_a_bit |
0 |
NaN |
NaN |
| Very_much |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
E12: Instructions from doctors or nurses
- E12. Have you ever received instructions from a doctor, nurse, or other health professional about who you should see for routine prostate cancer checkups or monitoring?
- 2=Yes
- 1=No
- 88=Don’t Know/not sure
e12 <- as.factor(d[,"e12"])
# Make "*" to NA
e12[which(e12=="*")]<-"NA"
levels(e12) <- list(No="1",
Yes="2",
Dont_know="88")
e12 <- ordered(e12, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, e12)
new.d <- apply_labels(new.d, e12 = "received instructions")
temp.d <- data.frame (new.d, e12)
result<-questionr::freq(temp.d$e12,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "e12")
e12
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
E13: # of PSA blood test
- E13. Since your prostate cancer diagnosis, how many times have you had a PSA blood test?
- 0=None
- 1=1
- 2=2
- 3=3
- 4=4 or more
- 88=Don’t know/not sure
e13 <- as.factor(d[,"e13"])
# Make "*" to NA
e13[which(e13=="*")]<-"NA"
levels(e13) <- list(None="0",
One="1",
Two="2",
Three="3",
Four_more="4",
Dont_know="88")
e13 <- ordered(e13, c("None","One","Two","Three","Four_more","Dont_know"))
new.d <- data.frame(new.d, e13)
new.d <- apply_labels(new.d, e13 = "times of PSA blood test")
temp.d <- data.frame (new.d, e13)
result<-questionr::freq(temp.d$e13,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "e13")
e13
| None |
0 |
NaN |
NaN |
| One |
0 |
NaN |
NaN |
| Two |
0 |
NaN |
NaN |
| Three |
0 |
NaN |
NaN |
| Four_more |
0 |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
E14: Be told PSA was rising
- E14. Since diagnosis or treatment, have you ever been told that your PSA was rising?
- 2=Yes
- 1=No
- 88=Don’t Know/not sure
e14 <- as.factor(d[,"e14"])
# Make "*" to NA
e14[which(e14=="*")]<-"NA"
levels(e14) <- list(No="1",
Yes="2",
Dont_know="88")
e14 <- ordered(e14, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, e14)
new.d <- apply_labels(new.d, e14 = "been told PSA was rising")
temp.d <- data.frame (new.d, e14)
result<-questionr::freq(temp.d$e14,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "e14")
e14
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
E15: Recurred or got worse
- E15. Since you were diagnosed, did your doctor ever tell you that your prostate cancer came back (recurred) or progressed (got worse)?
- 2=Yes
- 1=No
- 88=Don’t Know/not sure
e15 <- as.factor(d[,"e15"])
# Make "*" to NA
e15[which(e15=="*")]<-"NA"
levels(e15) <- list(No="1",
Yes="2",
Dont_know="88")
e15 <- ordered(e15, c("No","Yes","Dont_know"))
new.d <- data.frame(new.d, e15)
new.d <- apply_labels(new.d, e15 = "been told recurred progressed")
temp.d <- data.frame (new.d, e15)
result<-questionr::freq(temp.d$e15,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "e15")
e15
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
F1: Height
f1cm <- d[,"f1cm"]
new.d <- data.frame(new.d, f1cm)
new.d <- apply_labels(new.d, f1cm = "height in cm")
temp.d <- data.frame (new.d, f1cm)
result<-questionr::freq(temp.d$f1cm,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "How tall are you? (cm)")
How tall are you? (cm)
| 0 |
0 |
0 |
NA |
F2: Weight
- F2. How much do you current weight?
f2lbs <- d[,"f2lbs"]
new.d <- data.frame(new.d, f2lbs)
new.d <- apply_labels(new.d, f2lbs = "weight in lbs")
temp.d <- data.frame (new.d, f2lbs)
result<-questionr::freq(temp.d$f2lbs,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (lbs)")
How much do you current weight? (lbs)
| 0 |
0 |
0 |
NA |
f2kgs <- d[,"f2kgs"]
new.d <- data.frame(new.d, f2kgs)
new.d <- apply_labels(new.d, f2kgs = "weight in lbs")
temp.d <- data.frame (new.d, f2kgs)
result<-questionr::freq(temp.d$f2kgs,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "How much do you current weight? (kgs)")
How much do you current weight? (kgs)
| 0 |
0 |
0 |
NA |
F3: Exercise frequency
- F3. How many days per week do you typically get moderate or strenuous exercise (such as heavy lifting, shop work, construction or farm work, home repair, gardening, bowling, golf, jogging, basketball, riding a bike, etc.)?
- 4=5-7 times per week
- 3=3-4 times per week
- 2=1-2 times per week
- 1=Less than once per week/do not exercise
f3 <- as.factor(d[,"f3"])
# Make "*" to NA
f3[which(f3=="*")]<-"NA"
levels(f3) <- list(Per_week_5_7="4",
Per_week_3_4="3",
Per_week_1_2="2",
Per_week_less_1="1")
f3 <- ordered(f3, c("Per_week_5_7","Per_week_3_4","Per_week_1_2","Per_week_less_1"))
new.d <- data.frame(new.d, f3)
new.d <- apply_labels(new.d, f3 = "exercise")
temp.d <- data.frame (new.d, f3)
result<-questionr::freq(temp.d$f3,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "F3. How many days per week do you typically get moderate or strenuous exercise")
F3. How many days per week do you typically get moderate or strenuous exercise
| Per_week_5_7 |
0 |
NaN |
NaN |
NaN |
NaN |
| Per_week_3_4 |
0 |
NaN |
NaN |
NaN |
NaN |
| Per_week_1_2 |
0 |
NaN |
NaN |
NaN |
NaN |
| Per_week_less_1 |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
F4: Minutes of exercise
- F4. On those days that you do moderate or strenuous exercise, how many minutes did you typically exercise at this level?
- 2=Less than 30 minutes
- 3=30 minutes – 1 hour
- 4=More than 1 hour
- 1=Do not exercise
f4 <- as.factor(d[,"f4"])
# Make "*" to NA
f4[which(f4=="*")]<-"NA"
levels(f4) <- list(Less_than_30_min="2",
Between_30_min_1_hour="3",
More_than_1_hour="4",
Do_not_exercise="1")
f4 <- ordered(f4, c("Less_than_30_min","Between_30_min_1_hour","More_than_1_hour","Do_not_exercise"))
new.d <- data.frame(new.d, f4)
new.d <- apply_labels(new.d, f4 = "how many minutes exercise")
temp.d <- data.frame (new.d, f4)
result<-questionr::freq(temp.d$f4,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "F4")
F4
| Less_than_30_min |
0 |
NaN |
NaN |
NaN |
NaN |
| Between_30_min_1_hour |
0 |
NaN |
NaN |
NaN |
NaN |
| More_than_1_hour |
0 |
NaN |
NaN |
NaN |
NaN |
| Do_not_exercise |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
F5: Drink alcohol frequency
- F5. In the past month, about how often do you have at least one drink of any alcoholic beverage such as beer, wine, a malt beverage, or liquor? One drink is equivalent to a 12 oz beer, a 5 oz glass of wine, or a drink with one shot of liquor.
- 6=Everyday
- 5=5-6 times per week
- 4=3-4 times per week
- 3=1-2 times per week
- 2=Fewer than once per week
- 1=Did not drink
f5 <- as.factor(d[,"f5"])
# Make "*" to NA
f5[which(f5=="*")]<-"NA"
levels(f5) <- list(Everyday="6",
Per_week_5_6_times="5",
Per_week_3_4_times="4",
Per_week_1_2_times="3",
Per_week_fewer_once="2",
Not_drink="1")
f5 <- ordered(f5, c("Everyday","Per_week_5_6_times","Per_week_3_4_times","Per_week_1_2_times","Per_week_fewer_once","Not_drink"))
new.d <- data.frame(new.d, f5)
new.d <- apply_labels(new.d, f5 = "how often drink")
temp.d <- data.frame (new.d, f5)
result<-questionr::freq(temp.d$f5,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "f5")
f5
| Everyday |
0 |
NaN |
NaN |
NaN |
NaN |
| Per_week_5_6_times |
0 |
NaN |
NaN |
NaN |
NaN |
| Per_week_3_4_times |
0 |
NaN |
NaN |
NaN |
NaN |
| Per_week_1_2_times |
0 |
NaN |
NaN |
NaN |
NaN |
| Per_week_fewer_once |
0 |
NaN |
NaN |
NaN |
NaN |
| Not_drink |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
F6: How many drinks
- F6. When you drank during the past month, how many drinks do you have on a typical occasion?
- 3=3 or more drinks
- 2=1-2 drinks
- 1=Did not drink
f6 <- as.factor(d[,"f6"])
# Make "*" to NA
f6[which(f6=="*")]<-"NA"
levels(f6) <- list(Three_or_more="3",
One_to_two_drinks="2",
Not_drink="1")
f6 <- ordered(f6, c("Three_or_more","One_to_two_drinks","Not_drink"))
new.d <- data.frame(new.d, f6)
new.d <- apply_labels(new.d, f6 = "how many drinks")
temp.d <- data.frame (new.d, f6)
result<-questionr::freq(temp.d$f6,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "f6")
f6
| Three_or_more |
0 |
NaN |
NaN |
NaN |
NaN |
| One_to_two_drinks |
0 |
NaN |
NaN |
NaN |
NaN |
| Not_drink |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
F7: Smoking history
- F7. Have you ever smoked at least 100 cigarettes in your lifetime?
- F7Age. If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
- 555 = “Less than 10”
- 777 = “75+”
- F7a. How many cigarettes do you (or did you) usually smoke per day?
- 1=1-5
- 2=6-10
- 3=11-20
- 4=21-30
- 5=31+
- F7b. Have you quit smoking?
- F7BAge. If yes, At what age did you quit?
- 555 = “Less than 10”
- 777 = “75+”
f7 <- as.factor(d[,"f7"])
# Make "*" to NA
f7[which(f7=="*")]<-"NA"
levels(f7) <- list(Yes="2",
No="1")
f7 <- ordered(f7, c("No","Yes"))
new.d <- data.frame(new.d, f7)
new.d <- apply_labels(new.d, f7 = "smoke")
temp.d <- data.frame (new.d, f7)
result<-questionr::freq(temp.d$f7,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "F7. Have you ever smoked at least 100 cigarettes in your lifetime?")
F7. Have you ever smoked at least 100 cigarettes in your lifetime?
| No |
0 |
NaN |
NaN |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
f7age <- d[,"f7age"]
f7age[which(f7age=="555")]<-"Less_than_10"
f7age[which(f7age=="777")]<-"More_than_75"
new.d <- data.frame(new.d, f7age)
new.d <- apply_labels(new.d, f7age = "age start to smoke")
temp.d <- data.frame (new.d, f7age)
result<-questionr::freq(temp.d$f7age,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "F7Age. If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?")
F7Age. If yes, At what age did you start smoking on a regular basis (at least one cigarette/day)?
| 0 |
0 |
0 |
NA |
f7a <- as.factor(d[,"f7a"])
# Make "*" to NA
f7a[which(f7a=="*")]<-"NA"
levels(f7a) <- list(One_to_five="1",
Six_to_ten="2",
Eleven_to_twenty="3",
Twentyone_to_Thirty="4",
Older_31="5")
f7a <- ordered(f7a, c("One_to_five","Six_to_ten","Eleven_to_twenty","Twentyone_to_Thirty","Older_31"))
new.d <- data.frame(new.d, f7a)
new.d <- apply_labels(new.d, f7a = "How many cigarettes per day")
temp.d <- data.frame (new.d, f7a)
result<-questionr::freq(temp.d$f7a,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "F7a. How many cigarettes do you (or did you) usually smoke per day?")
F7a. How many cigarettes do you (or did you) usually smoke per day?
| One_to_five |
0 |
NaN |
NaN |
NaN |
NaN |
| Six_to_ten |
0 |
NaN |
NaN |
NaN |
NaN |
| Eleven_to_twenty |
0 |
NaN |
NaN |
NaN |
NaN |
| Twentyone_to_Thirty |
0 |
NaN |
NaN |
NaN |
NaN |
| Older_31 |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
f7b <- as.factor(d[,"f7b"])
# Make "*" to NA
f7b[which(f7b=="*")]<-"NA"
levels(f7b) <- list(No="1",
Yes="2")
new.d <- data.frame(new.d, f7b)
new.d <- apply_labels(new.d, f7b = "quit smoking")
temp.d <- data.frame (new.d, f7b)
result<-questionr::freq(temp.d$f7b,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "F7b. Have you quit smoking?")
F7b. Have you quit smoking?
| No |
0 |
NaN |
NaN |
| Yes |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
f7bage <- d[,"f7bage"]
f7bage[which(f7bage=="555")]<-"Less_than_10"
f7bage[which(f7bage=="777")]<-"More_than_75"
new.d <- data.frame(new.d, f7bage)
new.d <- apply_labels(new.d, f7bage = "age quit smoking")
temp.d <- data.frame (new.d, f7bage)
result<-questionr::freq(temp.d$f7bage,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "F7BAge. If yes, At what age did you quit?")
F7BAge. If yes, At what age did you quit?
| 0 |
0 |
0 |
NA |
G1: Marital status
- G1. What is your current marital status?
- 1=Married, or living with a partner
- 2=Separated
- 3=Divorced
- 4=Widowed
- 5=Never Married
g1 <- as.factor(d[,"g1"])
# Make "*" to NA
g1[which(g1=="*")]<-"NA"
levels(g1) <- list(Married_partner="1",
Separated="2",
Divorced="3",
Widowed="4",
Never_Married="5")
g1 <- ordered(g1, c("Married_partner","Separated","Divorced","Widowed","Never_Married"))
new.d <- data.frame(new.d, g1)
new.d <- apply_labels(new.d, g1 = "marital status")
temp.d <- data.frame (new.d, g1)
result<-questionr::freq(temp.d$g1,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "g1")
g1
| Married_partner |
0 |
NaN |
NaN |
NaN |
NaN |
| Separated |
0 |
NaN |
NaN |
NaN |
NaN |
| Divorced |
0 |
NaN |
NaN |
NaN |
NaN |
| Widowed |
0 |
NaN |
NaN |
NaN |
NaN |
| Never_Married |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
G2: With whom do you live
- G2. With whom do you live? Mark all that apply.
- G2_1: 1=Live alone
- G2_2: 1=A spouse or partner
- G2_3: 1=Other family
- G2_4: 1=Other people (non-family)
- G2_5: 1=Pets
g2_1 <- as.factor(d[,"g2_1"])
levels(g2_1) <- list(Live_alone="1")
new.d <- data.frame(new.d, g2_1)
new.d <- apply_labels(new.d, g2_1 = "Live alone")
temp.d <- data.frame (new.d, g2_1)
result<-questionr::freq(temp.d$g2_1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = " g2_1: Live alone")
g2_1: Live alone
| Live_alone |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
g2_2 <- as.factor(d[,"g2_2"])
levels(g2_2) <- list(spouse_partner="1")
new.d <- data.frame(new.d, g2_2)
new.d <- apply_labels(new.d, g2_2 = "A spouse or partner")
temp.d <- data.frame (new.d, g2_2)
result<-questionr::freq(temp.d$g2_2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = " g2_2: A spouse or partner")
g2_2: A spouse or partner
| spouse_partner |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
g2_3 <- as.factor(d[,"g2_3"])
levels(g2_3) <- list(Other_family="1")
new.d <- data.frame(new.d, g2_3)
new.d <- apply_labels(new.d, g2_3 = "Other family")
temp.d <- data.frame (new.d, g2_3)
result<-questionr::freq(temp.d$g2_3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = " g2_3: Other family")
g2_3: Other family
| Other_family |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
g2_4 <- as.factor(d[,"g2_4"])
levels(g2_4) <- list(Other_non_family="1")
new.d <- data.frame(new.d, g2_4)
new.d <- apply_labels(new.d, g2_4 = "Other people (non-family)")
temp.d <- data.frame (new.d, g2_4)
result<-questionr::freq(temp.d$g2_4,total = TRUE)
kable(result, format = "simple", align = 'l', caption = " g2_4: Other people (non-family)")
g2_4: Other people (non-family)
| Other_non_family |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
g2_5 <- as.factor(d[,"g2_5"])
levels(g2_5) <- list(Pets="1")
new.d <- data.frame(new.d, g2_5)
new.d <- apply_labels(new.d, g2_5 = "Pets")
temp.d <- data.frame (new.d, g2_5)
result<-questionr::freq(temp.d$g2_5,total = TRUE)
kable(result, format = "simple", align = 'l', caption = " g2_5: Pets")
g2_5: Pets
| Pets |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
G3: Identify yourself
- G3. How do you identify yourself?
- 1=Straight/heterosexual
- 2=Bisexual
- 3=Gay/homosexual/same gender loving
- 4=Other
- 99=Prefer not to answer
g3 <- as.factor(d[,"g3"])
# Make "*" to NA
g3[which(g3=="*")]<-"NA"
levels(g3) <- list(heterosexual="1",
Bisexual="2",
homosexual="3",
Other="4",
Prefer_not_to_answer="99")
g3 <- ordered(g3, c("heterosexual","Bisexual","homosexual","Other","Prefer_not_to_answer"))
new.d <- data.frame(new.d, g3)
new.d <- apply_labels(new.d, g3 = "identify yourself")
temp.d <- data.frame (new.d, g3)
result<-questionr::freq(temp.d$g3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = " g3")
g3
| heterosexual |
0 |
NaN |
NaN |
| Bisexual |
0 |
NaN |
NaN |
| homosexual |
0 |
NaN |
NaN |
| Other |
0 |
NaN |
NaN |
| Prefer_not_to_answer |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
G3 Other: Identify yourself
g3other <- d[,"g3other"]
new.d <- data.frame(new.d, g3other)
new.d <- apply_labels(new.d, g3other = "g3other")
temp.d <- data.frame (new.d, g3other)
result<-questionr::freq(temp.d$g3other, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "G3 Other")
G4: Education
- G4. What is the HIGHEST level of education you, your father, and your mother have completed?
- 1=Grade school or less
- 2=Some high school
- 3=High school graduate or GED
- 4=Vocational school
- 5=Some college
- 6=Associate’s degree
- 7=College graduate (Bachelor’s degree)
- 8=Some graduate education
- 9=Graduate degree
- 88=Don’t know
g4a <- as.factor(d[,"g4a"])
# Make "*" to NA
g4a[which(g4a=="*")]<-"NA"
levels(g4a) <- list(Grade_school_or_less="1",
Some_high_school="2",
High_school_graduate_GED="3",
Vocational_school="4",
Some_college="5",
Associate_degree="6",
College_graduate="7",
Some_graduate_education="8",
Graduate_degree="9")
g4a <- ordered(g4a, c("Grade_school_or_less","Some_high_school","High_school_graduate_GED","Vocational_school","Some_college","Associate_degree","College_graduate","Some_graduate_education","Graduate_degree"))
new.d <- data.frame(new.d, g4a)
new.d <- apply_labels(new.d, g4a = "education")
temp.d <- data.frame (new.d, g4a)
result<-questionr::freq(temp.d$g4a,total = TRUE)
kable(result, format = "simple", align = 'l', caption = " g4a: You")
g4a: You
| Grade_school_or_less |
0 |
NaN |
NaN |
| Some_high_school |
0 |
NaN |
NaN |
| High_school_graduate_GED |
0 |
NaN |
NaN |
| Vocational_school |
0 |
NaN |
NaN |
| Some_college |
0 |
NaN |
NaN |
| Associate_degree |
0 |
NaN |
NaN |
| College_graduate |
0 |
NaN |
NaN |
| Some_graduate_education |
0 |
NaN |
NaN |
| Graduate_degree |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
g4b <- as.factor(d[,"g4b"])
# Make "*" to NA
g4b[which(g4b=="*")]<-"NA"
levels(g4b) <- list(Grade_school_or_less="1",
Some_high_school="2",
High_school_graduate_GED="3",
Vocational_school="4",
Some_college="5",
Associate_degree="6",
College_graduate="7",
Some_graduate_education="8",
Graduate_degree="9",
Dont_know="88")
g4b <- ordered(g4b, c("Grade_school_or_less","Some_high_school","High_school_graduate_GED","Vocational_school","Some_college","Associate_degree","College_graduate","Some_graduate_education","Graduate_degree","Dont_know"))
new.d <- data.frame(new.d, g4b)
new.d <- apply_labels(new.d, g4b = "education-father")
temp.d <- data.frame (new.d, g4b)
result<-questionr::freq(temp.d$g4b,total = TRUE)
kable(result, format = "simple", align = 'l', caption = " g4b: Your father")
g4b: Your father
| Grade_school_or_less |
0 |
NaN |
NaN |
| Some_high_school |
0 |
NaN |
NaN |
| High_school_graduate_GED |
0 |
NaN |
NaN |
| Vocational_school |
0 |
NaN |
NaN |
| Some_college |
0 |
NaN |
NaN |
| Associate_degree |
0 |
NaN |
NaN |
| College_graduate |
0 |
NaN |
NaN |
| Some_graduate_education |
0 |
NaN |
NaN |
| Graduate_degree |
0 |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
g4c <- as.factor(d[,"g4c"])
# Make "*" to NA
g4c[which(g4c=="*")]<-"NA"
levels(g4c) <- list(Grade_school_or_less="1",
Some_high_school="2",
High_school_graduate_GED="3",
Vocational_school="4",
Some_college="5",
Associate_degree="6",
College_graduate="7",
Some_graduate_education="8",
Graduate_degree="9",
Dont_know="88")
g4c <- ordered(g4c, c("Grade_school_or_less","Some_high_school","High_school_graduate_GED","Vocational_school","Some_college","Associate_degree","College_graduate","Some_graduate_education","Graduate_degree","Dont_know"))
new.d <- data.frame(new.d, g4c)
new.d <- apply_labels(new.d, g4c = "education-mother")
temp.d <- data.frame (new.d, g4c)
result<-questionr::freq(temp.d$g4c,total = TRUE)
kable(result, format = "simple", align = 'l', caption = " g4c: Your mother")
g4c: Your mother
| Grade_school_or_less |
0 |
NaN |
NaN |
| Some_high_school |
0 |
NaN |
NaN |
| High_school_graduate_GED |
0 |
NaN |
NaN |
| Vocational_school |
0 |
NaN |
NaN |
| Some_college |
0 |
NaN |
NaN |
| Associate_degree |
0 |
NaN |
NaN |
| College_graduate |
0 |
NaN |
NaN |
| Some_graduate_education |
0 |
NaN |
NaN |
| Graduate_degree |
0 |
NaN |
NaN |
| Dont_know |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
G5: Job
- G5. Which one of the following best describes what you currently do?
- 1=Currently working full-time
- 2=Currently working part-time
- 3=Looking for work, unemployed
- 4=Retired
- 5=On disability permanently
- 6=On disability for a period of time (on sick leave or paternity leave or disability leave for other reasons)
- 7=Volunteer work/work without pay
- 8=Other
g5 <- as.factor(d[,"g5"])
# Make "*" to NA
g5[which(g5=="*")]<-"NA"
levels(g5) <- list(full_time="1",
part_time="2",
unemployed="3",
Retired="4",
disability_permanently="5",
disability_for_a_time="6",
Volunteer_work="7",
Other="8")
g5 <- ordered(g5, c("full_time","part_time","unemployed","Retired","disability_permanently","disability_for_a_time", "Volunteer_work","Other"))
new.d <- data.frame(new.d, g5)
new.d <- apply_labels(new.d, g5 = "job")
temp.d <- data.frame (new.d, g5)
result<-questionr::freq(temp.d$g5,total = TRUE)
kable(result, format = "simple", align = 'l', caption = " g5")
g5
| full_time |
0 |
NaN |
NaN |
| part_time |
0 |
NaN |
NaN |
| unemployed |
0 |
NaN |
NaN |
| Retired |
0 |
NaN |
NaN |
| disability_permanently |
0 |
NaN |
NaN |
| disability_for_a_time |
0 |
NaN |
NaN |
| Volunteer_work |
0 |
NaN |
NaN |
| Other |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
G5 Other: job
g5other <- d[,"g5other"]
new.d <- data.frame(new.d, g5other)
new.d <- apply_labels(new.d, g5other = "g5other")
temp.d <- data.frame (new.d, g5other)
result<-questionr::freq(temp.d$g5other, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "G5 Other")
G6: Health insurance
- G6. What kind of health insurance or health care coverage do you currently have? Mark all that apply.
- G6_1: 1=Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
- G6_2: 1=Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
- G6_3: 1=Insurance purchased directly from an insurance company (by you or another family member)
- G6_4: 1=Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
- G6_5: 1= Medicaid or other state provided insurance
- G6_6: 1=Medicare/government insurance
- G6_7: 1=VA/Military Facility (including those who have ever used or enrolled for VA health care)
- G6_8: 1=I do not have any medical insurance
g6_1 <- as.factor(d[,"g6_1"])
levels(g6_1) <- list(Insurance_employer="1")
new.d <- data.frame(new.d, g6_1)
new.d <- apply_labels(new.d, g6_1 = "Insurance_employer")
temp.d <- data.frame (new.d, g6_1)
result<-questionr::freq(temp.d$g6_1,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "G6_1. Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)")
G6_1. Insurance provided through my current or former employer or union (including Kaiser/HMO/PPO)
| Insurance_employer |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
g6_2 <- as.factor(d[,"g6_2"])
levels(g6_2) <- list(Insurance_family="1")
new.d <- data.frame(new.d, g6_2)
new.d <- apply_labels(new.d, g6_2 = "Insurance_family")
temp.d <- data.frame (new.d, g6_2)
result<-questionr::freq(temp.d$g6_2,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "G6_2. Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)")
G6_2. Insurance provided by another family member (e.g., spouse) through their current or former employer or union (including Kaiser/HMO/PPO)
| Insurance_family |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
g6_3 <- as.factor(d[,"g6_3"])
levels(g6_3) <- list(Insurance_insurance_company="1")
new.d <- data.frame(new.d, g6_3)
new.d <- apply_labels(new.d, g6_3 = "Insurance_insurance_company")
temp.d <- data.frame (new.d, g6_3)
result<-questionr::freq(temp.d$g6_3,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "G6_3. Insurance purchased directly from an insurance company (by you or another family member)")
G6_3. Insurance purchased directly from an insurance company (by you or another family member)
| Insurance_insurance_company |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
g6_4 <- as.factor(d[,"g6_4"])
levels(g6_4) <- list(Insurance_exchange="1")
new.d <- data.frame(new.d, g6_4)
new.d <- apply_labels(new.d, g6_4 = "Insurance_exchange")
temp.d <- data.frame (new.d, g6_4)
result<-questionr::freq(temp.d$g6_4,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "G6_4. Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)")
G6_4. Insurance purchased from an exchange (sometimes called Obamacare or the Affordable Care Act)
| Insurance_exchange |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
g6_5 <- as.factor(d[,"g6_5"])
levels(g6_5) <- list(Medicaid_state="1")
new.d <- data.frame(new.d, g6_5)
new.d <- apply_labels(new.d, g6_5 = "Medicaid_state")
temp.d <- data.frame (new.d, g6_5)
result<-questionr::freq(temp.d$g6_5,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "G6_5. Medicaid or other state provided insurance")
G6_5. Medicaid or other state provided insurance
| Medicaid_state |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
g6_6 <- as.factor(d[,"g6_6"])
levels(g6_6) <- list(Medicare_government="1")
new.d <- data.frame(new.d, g6_6)
new.d <- apply_labels(new.d, g6_6 = "Medicare_government")
temp.d <- data.frame (new.d, g6_6)
result<-questionr::freq(temp.d$g6_6,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "G6_6. Medicare/government insurance")
G6_6. Medicare/government insurance
| Medicare_government |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
g6_7 <- as.factor(d[,"g6_7"])
levels(g6_7) <- list(VA_Military="1")
new.d <- data.frame(new.d, g6_7)
new.d <- apply_labels(new.d, g6_7 = "VA_Military")
temp.d <- data.frame (new.d, g6_7)
result<-questionr::freq(temp.d$g6_7,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "G6_7. VA/Military Facility (including those who have ever used or enrolled for VA health care)")
G6_7. VA/Military Facility (including those who have ever used or enrolled for VA health care)
| VA_Military |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
g6_8 <- as.factor(d[,"g6_8"])
levels(g6_8) <- list(Do_not_have="1")
new.d <- data.frame(new.d, g6_8)
new.d <- apply_labels(new.d, g6_8 = "Do_not_have")
temp.d <- data.frame (new.d, g6_8)
result<-questionr::freq(temp.d$g6_8,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "G6_8. I do not have any medical insurance")
G6_8. I do not have any medical insurance
| Do_not_have |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
G7: Income
- G7. What is your best estimate of your TOTAL FAMILY INCOME from all sources, before taxes, in the last calendar year? “Total family income” refers to your income PLUS the income of all family members living in this household (including cohabiting partners, and armed forces members living at home). This includes money from pay checks, government benefit programs, child support, social security, retirement funds, unemployment benefits, and disability.
- 1=Less than $15,000
- 2=$15,000 to $35,999
- 3=$36,000 to $45,999
- 4=$46,000 to $65,999
- 5=$66,000 to $99,999
- 6=$100,000 to $149,999
- 7=$150,000 to $199,999
- 8= $200,000 or more
g7 <- as.factor(d[,"g7"])
# Make "*" to NA
g7[which(g7=="*")]<-"NA"
levels(g7) <- list(Less_than_15000="1",
Between_15000_35999="2",
Between_36000_45999="3",
Between_46000_65999="4",
Between_66000_99999="5",
Between_100000_149999= "6",
Between_150000_199999="7",
More_than_200000="8")
g7 <- ordered(g7, c("Less_than_15000","Between_15000_35999","Between_36000_45999","Between_46000_65999","Between_66000_99999","Between_100000_149999", "Between_150000_199999","More_than_200000"))
new.d <- data.frame(new.d, g7)
new.d <- apply_labels(new.d, g7 = "income")
temp.d <- data.frame (new.d, g7)
result<-questionr::freq(temp.d$g7,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = " g7")
g7
| Less_than_15000 |
0 |
NaN |
NaN |
NaN |
NaN |
| Between_15000_35999 |
0 |
NaN |
NaN |
NaN |
NaN |
| Between_36000_45999 |
0 |
NaN |
NaN |
NaN |
NaN |
| Between_46000_65999 |
0 |
NaN |
NaN |
NaN |
NaN |
| Between_66000_99999 |
0 |
NaN |
NaN |
NaN |
NaN |
| Between_100000_149999 |
0 |
NaN |
NaN |
NaN |
NaN |
| Between_150000_199999 |
0 |
NaN |
NaN |
NaN |
NaN |
| More_than_200000 |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
G8: # people supported by income
- G8. In the last calendar year, how many people, including yourself, were supported by your family income?
- 1=1
- 2=2
- 3=3
- 4=4
- 5=5 or more
g8 <- as.factor(d[,"g8"])
# Make "*" to NA
g8[which(g8=="*")]<-"NA"
levels(g8) <- list(One="1",
Two="2",
Three="3",
Four="4",
Five_or_more="5")
g8 <- ordered(g8, c("One","Two","Three","Four","Five_or_more"))
new.d <- data.frame(new.d, g8)
new.d <- apply_labels(new.d, g8 = "people supported by income")
temp.d <- data.frame (new.d, g8)
result<-questionr::freq(temp.d$g8,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = " g8")
g8
| One |
0 |
NaN |
NaN |
NaN |
NaN |
| Two |
0 |
NaN |
NaN |
NaN |
NaN |
| Three |
0 |
NaN |
NaN |
NaN |
NaN |
| Four |
0 |
NaN |
NaN |
NaN |
NaN |
| Five_or_more |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
G9: Worry about finance
- G9. How worried were you or your family about being able to pay your normal monthly bills, including rent, mortgage, and/or other costs:
- During young adult life (up to age 30):
- Age 31 (up to just before prostate cancer diagnosis):
- Current (from prostate cancer diagnosis to present):
- 1=Not at all worried
- 2=A little worried
- 3=Somewhat worried
- 4=Very worried
g9a <- as.factor(d[,"g9a"])
# Make "*" to NA
g9a[which(g9a=="*")]<-"NA"
levels(g9a) <- list(Not_worried="1",
A_little_worried="2",
Somewhat_worried="3",
Very_worried="4")
new.d <- data.frame(new.d, g9a)
new.d <- apply_labels(new.d, g9a = "young adult life")
temp.d <- data.frame (new.d, g9a)
result<-questionr::freq(temp.d$g9a,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "a. During young adult life (up to age 30)")
a. During young adult life (up to age 30)
| Not_worried |
0 |
NaN |
NaN |
| A_little_worried |
0 |
NaN |
NaN |
| Somewhat_worried |
0 |
NaN |
NaN |
| Very_worried |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
g9b <- as.factor(d[,"g9b"])
# Make "*" to NA
g9b[which(g9b=="*")]<-"NA"
levels(g9b) <- list(Not_worried="1",
A_little_worried="2",
Somewhat_worried="3",
Very_worried="4")
new.d <- data.frame(new.d, g9b)
new.d <- apply_labels(new.d, g9b = "age 31 up to before dx")
temp.d <- data.frame (new.d, g9b)
result<-questionr::freq(temp.d$g9b,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "b. Age 31 (up to just before prostate cancer diagnosis)")
b. Age 31 (up to just before prostate cancer diagnosis)
| Not_worried |
0 |
NaN |
NaN |
| A_little_worried |
0 |
NaN |
NaN |
| Somewhat_worried |
0 |
NaN |
NaN |
| Very_worried |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
g9c <- as.factor(d[,"g9c"])
# Make "*" to NA
g9c[which(g9c=="*")]<-"NA"
levels(g9c) <- list(Not_worried="1",
A_little_worried="2",
Somewhat_worried="3",
Very_worried="4")
new.d <- data.frame(new.d, g9c)
new.d <- apply_labels(new.d, g9c = "current")
temp.d <- data.frame (new.d, g9c)
result<-questionr::freq(temp.d$g9c,total = TRUE)
kable(result, format = "simple", align = 'l', caption = "c. Current (from prostate cancer diagnosis to present)")
c. Current (from prostate cancer diagnosis to present)
| Not_worried |
0 |
NaN |
NaN |
| A_little_worried |
0 |
NaN |
NaN |
| Somewhat_worried |
0 |
NaN |
NaN |
| Very_worried |
0 |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
G10:Own or rent a house
- G10. Is the home you live in:
- 1=Owned or being bought by you (or someone in the household)?
- 2=Rented for money?
- 3=Other
g10 <- as.factor(d[,"g10"])
# Make "*" to NA
g10[which(g10=="*")]<-"NA"
levels(g10) <- list(Owned="1",
Rented="2",
Other="3")
g10 <- ordered(g10, c("Owned","Rented","Other"))
new.d <- data.frame(new.d, g10)
new.d <- apply_labels(new.d, g10 = "Own or rent a house")
temp.d <- data.frame (new.d, g10)
result<-questionr::freq(temp.d$g10,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = " g10")
g10
| Owned |
0 |
NaN |
NaN |
NaN |
NaN |
| Rented |
0 |
NaN |
NaN |
NaN |
NaN |
| Other |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
G10 Other: Own or rent a house
g10other <- d[,"g10other"]
new.d <- data.frame(new.d, g10other)
new.d <- apply_labels(new.d, g10other = "g10other")
temp.d <- data.frame (new.d, g10other)
result<-questionr::freq(temp.d$g10other, total = TRUE)
kable(result, format = "simple", align = 'l', caption = "G10 Other")
G11:Lose current sources
- G11. If you lost all your current source(s) of household income (your paycheck, public assistance, or other forms of income), how long could you continue to live at your current address and standard of living?
- 1=Less than 1 month
- 2=1 to 2 months
- 3=3 to 6 months
- 4=More than 6 months
g11 <- as.factor(d[,"g11"])
# Make "*" to NA
g11[which(g11=="*")]<-"NA"
levels(g11) <- list(Less_than_1_month="1",
One_to_two_month="2",
Three_to_six_month="3",
More_than_6_months="4")
g11 <- ordered(g11, c("Less_than_1_month","One_to_two_month","Three_to_six_month","More_than_6_months"))
new.d <- data.frame(new.d, g11)
new.d <- apply_labels(new.d, g11 = "ose current sources")
temp.d <- data.frame (new.d, g11)
result<-questionr::freq(temp.d$g11,cum=TRUE,total = TRUE)
kable(result, format = "simple", align = 'l', caption = " g11")
g11
| Less_than_1_month |
0 |
NaN |
NaN |
NaN |
NaN |
| One_to_two_month |
0 |
NaN |
NaN |
NaN |
NaN |
| Three_to_six_month |
0 |
NaN |
NaN |
NaN |
NaN |
| More_than_6_months |
0 |
NaN |
NaN |
NaN |
NaN |
| Total |
0 |
NaN |
100 |
100 |
100 |
G12: Today’s date
- G12. Please enter today’s date.
g12 <- as.Date(d[ , "g12"], format="%m/%d/%y")
new.d <- data.frame(new.d, g12)
new.d <- apply_labels(new.d, g12 = "today’s date")
#temp.d <- data.frame (new.d.1, g12)
summarytools::view(dfSummary(new.d$g12, style = 'grid',
max.distinct.values = 5, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
g12
[labelled, Date] |
today’s date |
|
|
|
0
(NaN%) |
Generated by summarytools 1.0.0 (R version 3.6.3)
2021-12-09